Detector for identifying at least one material property

Abstract

The invention relates to a detector (110) for detecting at least one material property m. The detector 110 comprises at least one sensor element (116) comprising a matrix (118) of optical sensors (120), the optical sensors (120) each having a photosensitive area (122). The sensor element (116) is configured to record at least one reflected image of the light beam originating from the at least one object (112). The detector (110) comprises at least one evaluation device (132) configured to determine a material characteristic by evaluating at least one beam profile of the reflected image.

Description

[0001] This application is a divisional application of the application with the filing date of March 13, 2020, PCT international application number PCT / EP2020 / 056759, Chinese national phase application number 202080021006.0, and invention title "Detector for Identifying at Least One Material Property". Technical Field

[0002] The present invention relates to a detector, a detector system and a method for determining at least one material property of at least one object. The present invention further relates to: a human-machine interface for exchanging at least one piece of information between a user and a machine; an entertainment device; a tracking system; a camera; a scanning system; and various uses of the detector device. The device, method and uses according to the present invention can specifically be used, for example, in various fields including daily life, security technology, gaming, traffic technology, production technology, photography (such as digital photography or video photography for artistic, documentary or technical purposes), security technology, information technology, agriculture, crop protection, maintenance, cosmetics, medical technology or science. However, other applications are also possible. Background Art

[0003] A large number of methods for material classification and identification are known in the prior art. For example, material identification can be used in machine vision applications, medical applications and security applications for image classification purposes or gesture discrimination algorithms.

[0004] For example, US2016 / 0206216 A1 describes a device, system and method for skin detection. The device includes a thermal sensor input for obtaining thermal sensor data of a scene, a light sensor input for obtaining light sensor data of the scene, and an evaluation unit for analyzing the obtained thermal sensor data and the obtained light sensor data and for detecting a skin area within the scene based on the analysis. US2016 / 155006 A1 describes a device and a corresponding method for skin detection. The device includes: an illumination unit configured to project a predetermined illumination pattern onto a scene; an imaging unit configured to acquire an image of the scene; and an evaluation unit configured to evaluate the acquired image by analyzing the imaging illumination pattern reproduced in the image, and to detect a skin area within the image and to distinguish the skin area from non-skin areas within the image based on the analysis.

[0005] Material classification and identification are usually done in through beam mode, i.e., shining light through the sample and analyzing the extinction. Additionally, material classification and identification can be performed in reflection mode in a controlled environment. However, in reflection mode, a wavelength greater than 1000 nm is typically required to produce reliable results. Therefore, despite the advantages of known methods for material identification, there is still a need for new concepts for reliable identification of materials. Specifically, there is a great need to identify materials based on the reflected beam profile. However, most image filters for beam profile analysis (BPA) produce features that depend on both distance and material, making reliable identification and classification of materials impossible. A typical example is the width of the beam profile reflected from a translucent material.

[0006] DE 198 46 619A1 describes a device for determining the surface appearance quality of a structured surface, which evaluates electrical measurement signals from a photoelectric sensor array to derive a structure code that characterizes the structure-related properties of the measured surface.

[0007] CN 108 363 482A describes a method for controlling a smart TV via three-dimensional gestures based on binocular structured light. The method includes the following steps: synchronously acquiring images through a binocular camera; reconstructing a three-dimensional image based on the obtained left and right views; performing preprocessing; discriminating gesture actions after segmenting the three-dimensional gestures; and converting the discriminated gesture actions into operation instructions for the smart TV and executing the operation instructions.

[0008] US2018 / 033146 A1 describes a system and method for determining a depth map and a reflectance map from a structured light image. The depth map can be determined by capturing a structured light image and then using a triangulation method to determine the depth map based on points in the captured structured light image. The reflectance map can be determined based on the depth map and based on performing additional analysis on points in the captured structured light image. Summary of the Invention

[0009] Problems to be Solved by the Invention

[0010] Therefore, an object of the present invention is to provide devices and methods in the face of the above technical challenges of known devices and methods. Specifically, the object of the present invention is to provide devices and methods that can reliably identify at least one material property of an object, preferably with low technical effort and low requirements in terms of technical resources and cost. Overview of the Invention

[0012] The present invention with the features of the independent claims solves this problem. Advantageous developments of the present invention that can be implemented individually or in combination are presented in the dependent claims and / or the following description and detailed embodiments.

[0013] As used below, the terms "comprising", "including" or "containing" or any grammatical variations thereof are used in a non-exclusive manner. Thus, these terms can refer both to cases where no other features are present in the entity described in the context other than the features introduced by these terms, and also to cases where one or more other features are present in addition to the features introduced by these terms. As an example, the expressions "A comprises B", "A includes B" and "A contains B" can refer to cases where no other elements are present in A other than B (i.e., cases where A consists solely and uniquely of B), and also to cases where one or more other elements are present in entity A in addition to B, such as element C, elements C and D, or even other elements.

[0014] Furthermore, it should be noted that the terms "at least one", "one or more" or similar expressions indicating that a feature or element may be present once or more than once are generally used only once when introducing the corresponding feature or element. Below, in most cases, when referring to the corresponding feature or element, the expressions "at least one" or "one or more" will not be repeated, even though the corresponding feature or element may be present only once or more than once.

[0015] Furthermore, as used below, the terms "preferably", "more preferably", "particularly", "more particularly", "specifically", "more specifically" or similar terms are used in connection with optional features without restricting alternative possibilities. Thus, the features introduced by these terms are optional features and are not intended to limit the scope of the claims in any way. As will be recognized by those skilled in the art, the present invention can be carried out by using alternative features. Similarly, features introduced by "in an embodiment of the present invention" or similar expressions are intended to be optional features, without any limitation to alternative embodiments of the present invention, without any limitation to the scope of the present invention, and without any limitation to the possibility of combining the features introduced in this way with other optional or non-optional features of the present invention.

[0016] In a first aspect of the present invention, a detector for identifying at least one material property m is disclosed.

[0017] As used herein, the term "material property" refers to at least one arbitrary property of a material that is configured for the characterization and / or identification and / or classification of the material. For example, the material property can be a property selected from the group including the following: roughness, the penetration depth of light into the material, the property characterizing the material as a biological or non-biological material, reflectivity, specular reflectivity, diffuse reflectivity, surface properties, a measure of translucency, scattering (specifically backscattering behavior), etc. The at least one material property can be a property selected from the group including the following: scattering coefficient, translucency, transparency, deviation from Lambertian surface reflection, speckle, etc. As used herein, the term "identifying at least one material property" refers to determining a material property and assigning it to one or more of the objects. The detector can include at least one database that includes a list and / or table having predefined and / or predetermined material properties, such as a look-up list or a look-up table. The list and / or table of material properties can be determined and / or generated by performing at least one test measurement using the detector according to the present invention, for example, by performing a material test using a sample having known material properties. The list and / or table of material properties can be determined and / or generated at the manufacturer's site and / or by the user of the detector. The material property can be additionally assigned to a material classifier, such as one or more of a material name, a material group (such as biological or non-biological material, translucent or non-translucent material, metal or non-metal, skin or non-skin, fur or non-fur, carpet or non-carpet, reflective or non-reflective, specularly reflective or non-specularly reflective, foam or non-foam, hair or non-hair, roughness group, etc.). The detector can include at least one database that includes a list and / or table having material properties and associated material names and / or material groups.

[0018] Specifically, the detector can be configured for the detection of biological tissue, specifically, human skin. As used herein, the term "biological tissue" generally refers to biological material that contains living cells. The detector can be a device for detecting, specifically, for optical detection of biological tissue (specifically, human skin). The term "detection of biological tissue" refers to determining and / or verifying whether the surface to be examined or tested is or includes biological tissue (specifically, human skin), and / or differentiating biological tissue (specifically, human skin) from other tissues (specifically, other surfaces), and / or differentiating different types of biological tissue, such as differentiating different types of human tissue, e.g., muscle, fat, organs, etc. For example, biological tissue can be or can include human tissue or a part thereof, such as skin, hair, muscle, fat, organs, etc. For example, biological tissue can be or can include animal tissue or a part thereof, such as skin, fur, muscle, fat, organs, etc. For example, biological tissue can be or can include plant tissue or a part thereof. The detector can be adapted to differentiate animal tissue or a part thereof from one or more of inorganic tissue, such as agricultural machinery or milking machines, metal surfaces, plastic surfaces. The detector can be adapted to differentiate plant tissue or a part thereof from one or more of inorganic tissue, such as agricultural machinery, metal surfaces, plastic surfaces. The detector can be adapted to differentiate food and / or beverage from plates and / or glasses. The detector can be adapted to differentiate different types of food, such as fruits, meat, and fish. The detector can be adapted to differentiate cosmetics and / or applied cosmetics from human skin. The detector can be adapted to differentiate human skin from foam, paper, wood, monitors, screens. The detector can be adapted to differentiate human skin from fabrics. The detector can be adapted to differentiate maintenance products from the materials of machine components (such as metal components, etc.). The detector can be adapted to differentiate organic materials from inorganic materials. The detector can be adapted to differentiate human biological tissue from the surfaces of artificial or inanimate objects. The detector can be particularly used for non-therapeutic and non-diagnostic applications.

[0019] The detector can be a stationary device or a mobile device. Additionally, the detector can be an independent device or can form part of another device (such as a computer, vehicle, or any other device). Additionally, the detector can be a handheld device. Other embodiments of the detector are feasible.

[0020] A detector for identifying at least one material property m, comprising

[0021] - at least one sensor element, which comprises a matrix of optical sensors, each optical sensor having a photosensitive area, wherein the sensor element is configured to record at least one reflected image of a light beam originating from at least one object;

[0022] - at least one evaluation device configured to determine material properties by evaluating at least one beam profile of a reflected image,

[0023] wherein the evaluation device is configured to determine at least one distance feature by applying at least one distance-dependent image filter Ф1 to the reflected image wherein the distance-dependent image filter is at least one filter selected from the group comprising: a depth-from-photon-ratio filter; a depth-from-defocus filter; or a linear combination thereof; or is another distance-dependent image filter Ф 1其他 (Ф 1other ) which is related to the depth-from-photon-ratio filter and / or the depth-from-defocus filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, wherein Ф z is one of the depth-from-photon-ratio filter and / or the depth-from-defocus filter or a linear combination thereof, wherein the evaluation device is configured to determine at least one material feature by applying at least one material-dependent image filter Ф2 to the reflected image

[0024] wherein the evaluation device is configured to determine the ordinate z and the material property m by evaluating the distance feature and the material feature

[0025] ​As used herein, the term "sensor element" generally refers to a device or a combination of devices configured to sense at least one parameter. In this case, the parameter can specifically be an optical parameter, and the sensor element can specifically be an optical sensor element. The sensor element can be formed as a single individual device or as a combination of multiple devices. The sensor element includes a matrix of optical sensors. The sensor element can include at least one CMOS sensor. The matrix can be composed of independent pixels (such as independent optical sensors). Thus, an inorganic photodiode matrix can be formed. However, alternatively, a commercially available matrix can be used, such as one or more of a CCD detector (such as a CCD detector chip) and / or a CMOS detector (such as a CMOS detector chip). Thus, generally, the sensor element can be and / or can include at least one CCD and / or CMOS device and / or the optical sensors can form a sensor array or can be part of a sensor array (such as the aforementioned matrix). Thus, as an example, the sensor element can include a pixel array, such as a rectangular array, having m rows and n columns, where m and n are independently positive integers. Preferably, there are more than one column and more than one row, i.e., n > 1, m > 1. Thus, as an example, n can be from 2 to 16 or higher and m can be from 2 to 16 or higher. Preferably, the ratio of the number of rows to the number of columns is close to 1. As an example, n and m can be selected such that 0.3 ≤ m / n ≤ 3, such as by selecting m / n = 1:1, 4:3, 16:9 or similar values. As an example, the array can be a square array, having the same number of rows and columns, such as by selecting m = 2, n = 2 or m = 3, n = 3, etc.

[0026] Specifically, the matrix can be a rectangular matrix having at least one row (preferably multiple rows) and multiple columns. As an example, the rows and columns can be oriented substantially vertically. As used herein, the term "substantially vertical" refers to the condition of vertical orientation with a tolerance of, for example, ±20° or less, preferably ±10° or less, more preferably ±5° or less. Similarly, the term "substantially parallel" refers to the condition of parallel orientation with a tolerance of, for example, ±20° or less, preferably ±10° or less, more preferably ±5° or less. Thus, as an example, a tolerance of less than 20°, particularly less than 10° or even less than 5° is acceptable. To provide a wide range of views, the matrix can particularly have at least 10 rows, preferably at least 500 rows, more preferably at least 1000 rows. Similarly, the matrix can have at least 10 columns, preferably at least 500 columns, more preferably at least 1000 columns. The matrix can include at least 50 optical sensors, preferably at least 100,000 optical sensors, more preferably at least 5,000,000 optical sensors. The matrix can include multiple pixels in the range of millions of pixels. However, other embodiments are feasible. Thus, in a setting where axial rotational symmetry is desired, a circular or concentric arrangement of the optical sensors (which can also be referred to as pixels) of the matrix may be preferred.

[0027] Thus, as an example, the sensor element can be part of a pixelated optical device or can constitute a pixelated optical device. For example, the sensor element can be and / or can include at least one CCD and / or CMOS device. As an example, the sensor element can be part of at least one CCD and / or CMOS device having a pixel matrix or can constitute at least one CCD and / or CMOS device having a pixel matrix, where each pixel forms a photosensitive region. The sensor element can use a rolling shutter or global shutter method to read out the matrix of optical sensors.

[0028] As used herein, an "optical sensor" generally refers to a light-sensitive device for detecting a light beam, such as for detecting illumination and / or a light spot generated by at least one light beam. As further used herein, a "photosensitive region" generally refers to a region of an optical sensor that can be externally illuminated by at least one light beam, and that region generates at least one sensor signal in response to the illumination. The photosensitive region may specifically be located on the surface of the corresponding optical sensor. However, other embodiments are possible. The detector may include a plurality of optical sensors, each optical sensor having a photosensitive region. As used herein, the term "each optical sensor has at least one photosensitive region" refers to a configuration having a plurality of single optical sensors, each single optical sensor having one photosensitive region, and refers to a configuration with a combined optical sensor including a plurality of photosensitive regions. Thus, the term "optical sensor" additionally refers to a light-sensitive device configured to generate at least one output signal. In the case where the detector includes a plurality of optical sensors, each optical sensor may be embodied such that exactly one photosensitive region exists in the corresponding optical sensor, such as by providing exactly one photosensitive region that can be illuminated, and in response to that illumination, exactly one uniform sensor signal is created for the entire optical sensor. Thus, each optical sensor may be a single-region optical sensor. However, the use of single-region optical sensors makes the setup of the detector particularly simple and efficient. Thus, as an example, commercially available optical sensors, such as commercially available silicon photodiodes, each having exactly one photosensitive region, may be used in the setup. However, other embodiments are possible.

[0029] Specifically, the optical sensor may be or may include at least one photodetector, preferably an inorganic photodetector, more preferably an inorganic semiconductor photodetector, and most preferably a silicon photodetector. Specifically, the optical sensor may be sensitive in the infrared spectral range. All pixels in the matrix or at least a group of optical sensors in the matrix may specifically be the same. A group of the same pixels in the matrix may be specifically provided for different spectral ranges, or all pixels may be the same in terms of spectral sensitivity. Additionally, the pixels may be the same in size and / or with respect to their electronic or optoelectronic properties. Specifically, the optical sensor may be or may include an inorganic photodiode sensitive in the infrared spectral range (preferably in the range from 700 nm to 3.0 micrometers). Specifically, the optical sensor is sensitive in a part of the near-infrared region, where silicon photodiodes are specifically suitable for the range from 700 nm to 1100 nm. The infrared optical sensors that can be used for the optical sensor may be commercially available infrared optical sensors, such as those from trinamiX, D-67056 Ludwigshafen am Rhein, Germany TMGmbH under the trademark name Hertz-stueck TM Commercially available infrared optical sensors. Thus, by way of example, the optical sensor may include at least one optical sensor of the intrinsic photovoltaic type, and more preferably, at least one semiconductor photodiode selected from the group consisting of: Ge photodiodes, InGaAs photodiodes, extended InGaAs photodiodes, InAs photodiodes, InSb photodiodes, HgCdTe photodiodes. Additionally or alternatively, the optical sensor may include at least one optical sensor of the extrinsic photovoltaic type, and more preferably, at least one semiconductor photodiode selected from the group consisting of: Ge:Au photodiodes, Ge:Hg photodiodes, Ge:Cu photodiodes, Ge:Zn photodiodes, Si:Ga photodiodes, Si:As photodiodes. Additionally or alternatively, the optical sensor may include: at least one photoconductive sensor, such as a PbS or PbSe sensor; a bolometer, preferably a bolometer selected from the group consisting of VO bolometers and amorphous Si bolometers.

[0030] The optical sensor may be sensitive in one or more of the ultraviolet, visible or infrared spectral ranges. Specifically, the optical sensor may be sensitive in the visible spectral range from 500 nm to 780 nm, most preferably from 650 nm to 750 nm, or from 690 nm to 700 nm. Specifically, the optical sensor may be sensitive in the near-infrared region. Specifically, the optical sensor may be sensitive in a portion of the near-infrared region, where silicon photodiodes are particularly suitable for the range from 700 nm to 1000 nm. Specifically, the optical sensor may be sensitive in the infrared spectral range, specifically in the range from 780 nm to 3.0 microns. For example, each optical sensor independently may be or may include at least one element selected from the group consisting of photodiodes, phototubes, photoconductors, phototransistors or any combination thereof. For example, the optical sensor may be or may include at least one element selected from the group consisting of CCD sensor elements, CMOS sensor elements, photodiodes, phototubes, photoconductors, phototransistors or any combination thereof. Any other type of photosensitive element may be used. As will be outlined in more detail below, the photosensitive element may generally be made entirely or partially of inorganic materials and / or may be made entirely or partially of organic materials. Most commonly, as will be outlined in more detail below, one or more photodiodes, such as commercially available photodiodes, e.g., inorganic semiconductor photodiodes, may be used.

[0031] Preferably, the photosensitive area may be oriented substantially perpendicular to the optical axis of the detector. The optical axis may be a straight optical axis or may be bent or even split, such as by using one or more deflection elements and / or by using one or more beam splitters, where in the latter case the substantially perpendicular orientation may refer to the local optical axis in the respective branch or beam path of the optical setup.

[0032] Specifically, the photosensitive area may be oriented towards the object. As used herein, the term "oriented towards the object" generally refers to the case where the corresponding surface of the photosensitive area is completely or partially visible from the object. Specifically, at least one interconnecting line between at least one point of the object and at least one point in the corresponding photosensitive area may form an angle other than 0° with the surface element of the photosensitive area, such as an angle in the range of 20° to 90°, preferably in the range of 80 to 90°, such as an angle of 90°. Thus, when the object is on or near the optical axis, the light beam propagating from the object towards the detector may be substantially parallel to the optical axis. As used herein, the term "substantially perpendicular" refers to the condition of a perpendicular orientation, for example, with a tolerance of ±20° or less, preferably ±10° or less, more preferably ±5° or less. Similarly, the term "substantially parallel" refers to the condition of a parallel orientation, having, for example, a tolerance of ±20° or less, preferably ±10° or less, more preferably ±5° or less.

[0033] The sensor element is configured to record at least one reflected image of a light beam originating from at least one object. As used herein, the term "reflected image" refers to an image determined by the sensor element that includes at least one reflection feature. As further used herein but not limited to, the term "reflected image" may specifically relate to data recorded by using the sensor element, such as multiple electronic readings from an imaging device (such as the pixels of the sensor element). Thus, the reflected image itself may include pixels, where the pixels of the image are related to the pixels in the sensor element matrix. Thus, when referring to "pixels", it either refers to the unit of image information generated by a single pixel of the sensor element or directly refers to a single pixel of the sensor element. As used herein, the term "reflection feature" refers to a feature in the image plane generated by the object in response to illumination having, for example, at least one illumination feature. As used herein, the term "determine at least one reflection feature" refers to imaging and / or recording at least one light beam generated by the object in response to illumination with a light beam (specifically, having at least one illumination feature). In particular, the sensor element may be configured to at least determine the reflected image and / or image and / or record the reflected image. The reflected image may include at least one reflection pattern, and the at least one reflection pattern includes at least one reflection feature.

[0034] The detector includes at least one illumination source. The detector may include at least one illumination source configured to illuminate an object with at least one illumination beam. The illumination source may be configured to project at least one illumination pattern including at least one illumination feature onto at least one surface of the object. As used herein, the term "at least one illumination source" refers to at least one arbitrary device configured to provide at least one illumination beam (specifically, at least one illumination pattern) for illumination of the object. The illumination source may be adapted to illuminate the object directly or indirectly, where the illumination beam is reflected or scattered by the object and is thus at least partially directed towards the detector. The illumination source may be adapted to illuminate the object, for example, by directing a light beam towards the object, which reflects the light beam.

[0035] The illumination source may include at least one light source. The illumination source may include a plurality of light sources. The illumination source may include an artificial illumination source, specifically, at least one laser source and / or at least one incandescent lamp and / or at least one semiconductor light source, such as at least one light-emitting diode, specifically, organic and / or inorganic light-emitting diodes. As an example, the light emitted by the illumination source may have a wavelength in the range of 300 to 1100 nm (particularly, 500 to 1100 nm). Additionally or alternatively, light in the infrared spectral range (such as in the range of 780 nm to 3.0 μm) may be used. Specifically, light in the near-infrared region, specifically in the range of 700 nm to 1100 nm, which is applicable to a part of silicon photodiodes, may be used. Using light in the near-infrared region makes the light undetectable or only weakly detectable by the human eye and still detectable by silicon sensors, particularly standard silicon sensors. The illumination source may be adapted to emit light of a single wavelength. In other embodiments, the illumination may be adapted to emit light having multiple wavelengths, thus allowing additional measurements in other wavelength channels. The light source may be or may include at least one multi-beam light source. For example, the light source may include at least one laser source and one or more diffractive optical elements (DOEs).

[0036] Specifically, the illumination source may include at least one laser and / or laser source. Various types of lasers can be employed, such as semiconductor lasers, double heterostructure lasers, external cavity lasers, separate confinement heterostructure lasers, quantum cascade lasers, distributed Bragg reflector lasers, polariton lasers, hybrid silicon lasers, extended cavity diode lasers, quantum dot lasers, volume Bragg grating lasers, indium arsenide lasers, transistor lasers, diode-pumped lasers, distributed feedback lasers, quantum well lasers, interband cascade lasers, gallium arsenide lasers, semiconductor ring lasers, extended cavity diode lasers, or vertical cavity surface emitting lasers. Additionally or alternatively, non-laser light sources, such as LEDs and / or bulbs, can be used. The illumination source may include one or more diffractive optical elements (DOEs) adapted to generate an illumination pattern. For example, the illumination source may be adapted to generate and / or project a point cloud. For example, the illumination source may include one or more of at least one digital light processing projector, at least one LCoS projector, at least one spatial light modulator; at least one diffractive optical element; at least one light emitting diode array; at least one laser light source array. Considering their generally defined beam profiles and other operability characteristics, it is particularly preferred to use at least one laser source as the illumination source. The illumination source may be integrated into the housing of the detector.

[0037] Specifically, the illumination source may be configured to emit light in the infrared spectral range. However, it should be noted that additionally or alternatively, other spectral ranges are also feasible. Furthermore, the illumination source may specifically be configured to emit modulated or unmodulated light. In the case of using multiple illumination sources, different illumination sources may have different modulation frequencies, which, as outlined in further detail below, can later be used to distinguish the light beams. The detector may be configured to evaluate a single light beam or multiple light beams. In the case where multiple light beams propagate from the object to the detector, means for distinguishing the light beams may be provided. Thus, the light beams may have different spectral characteristics, and the detector may include one or more wavelength selection elements for distinguishing different light beams. Each light beam in the light beams can then be evaluated independently. As an example, the wavelength selection element may be or may include one or more filters, one or more prisms, one or more gratings, one or more dichroic mirrors, or any combination thereof. Additionally, additionally or alternatively, in order to distinguish two or more light beams, the light beams may be modulated in a specific manner. Thus, as an example, the light beams may be frequency modulated, and the sensor signals may be demodulated in order to partially distinguish the sensor signals originating from different light beams based on their demodulation frequencies. These techniques are generally known to those skilled in the field of high-frequency electronics. Generally, the evaluation means may be configured to distinguish different light beams having different modulations.

[0038] The illumination beam can generally be parallel to the optical axis or inclined with respect to the optical axis, for example including an angle with the optical axis. The detector can be configured such that the illumination beam propagates from the detector along the optical axis of the detector towards the object. For this purpose, the detector can include at least one reflective element, preferably at least one prism, for deflecting the illumination beam onto the optical axis. As an example, the illumination beam (such as a laser beam) and the optical axis can include an included angle of less than 10°, preferably less than 5°, or even less than 2°. However, other embodiments are feasible. In addition, the illumination beam can be on the optical axis or off the optical axis. As an example, the illumination beam can be parallel to the optical axis with a distance from the optical axis of less than 10 mm, preferably less than 5 mm or even less than 1 mm from the optical axis, or can even coincide with the optical axis.

[0039] Specifically, the illumination source and the optical sensor can be arranged in a common plane or in different planes. The illumination source and the optical sensor can have different spatial orientations. In particular, the illumination source and the sensor element can be arranged in a skewed arrangement.

[0040] The illumination source can be configured to generate at least one illumination pattern for illuminating an object. The illumination pattern can include at least one pattern selected from the group consisting of: at least one dot pattern, in particular a pseudo-random dot pattern; a random dot pattern or a quasi-random pattern; at least one Sobol pattern; at least one quasi-periodic pattern; at least one pattern including at least one known feature; at least one regular pattern; at least one triangular pattern; at least one hexagonal pattern; at least one rectangular pattern; at least one pattern including a convex uniform tiling; at least one line pattern including at least one line; at least one line pattern including at least two lines such as parallel lines or crossing lines. As used herein, the term "pattern" refers to any known or predetermined arrangement including at least one feature of any shape. The pattern can include at least one feature such as a dot or a symbol. The pattern can include multiple features. The pattern can include an arrangement of periodic or non-periodic features. As used herein, the term "at least one illumination pattern" refers to at least one arbitrary pattern including at least one illumination feature suitable for illuminating at least a part of an object. As used herein, the term "illumination feature" refers to at least one feature of the pattern that extends at least in part. The illumination pattern can include a single illumination feature. The illumination pattern can include multiple illumination features. For example, the illumination pattern can include at least one line pattern. For example, the illumination pattern can include at least one stripe pattern. For example, the illumination pattern can include at least one checkerboard pattern. For example, the illumination pattern can include at least one pattern having an arrangement of periodic or non-periodic features. The illumination pattern can include regular and / or constant and / or periodic patterns such as triangular patterns, rectangular patterns, hexagonal patterns or patterns including other convex tilings. The illumination pattern can exhibit at least one illumination feature selected from the group consisting of: at least one dot; at least one line; at least two lines such as parallel lines or crossing lines; at least one dot and one line; at least one arrangement of periodic or non-periodic features; at least one feature of any shape. For example, the illumination source can be adapted to generate and / or project a point cloud. The distance between two features of the illumination pattern and / or the area of at least one illumination feature can depend on the circle of confusion in the image. The illumination source can include at least one light source configured to generate at least one illumination pattern. Specifically, in order to generate and project the illumination pattern, the illumination source can include at least one laser source and at least one diffractive optical element (DOE). The detector can include at least one dot projector adapted to project at least one dot pattern, such as at least one laser source and DOE. As further used herein, the term "project at least one illumination pattern" refers to providing at least one illumination pattern for illuminating at least one object. The projected illumination pattern may be few, as there may be only a single illumination feature, such as a single dot. To increase reliability, the illumination pattern can include several illumination features, such as several dots.If the pattern is sparse, a single image can be used to perform both biological tissue detection and facial recognition simultaneously.

[0041] For example, the illumination source can include at least one line laser. The line laser can be adapted to send a laser line to an object, such as a horizontal or vertical laser line. The illumination source can include multiple line lasers. For example, the illumination source can include at least two line lasers, and the at least two line lasers can be arranged such that the illumination pattern includes at least two parallel or intersecting lines. The illumination source can include at least one light projector, and the at least one light projector is adapted to generate a point cloud such that the illumination pattern can include multiple point patterns. The illumination source can include at least one mask, and the at least one mask is adapted to generate an illumination pattern based on at least one light beam generated by the illumination source. The illumination source can be one attached to or integrated into a mobile device such as a smart phone. The illumination source can be used for other functions used in determining an image, such as an autofocus function. The illumination source can be integrated into the mobile device or attached to the mobile device, such as by using a connector such as USB or a phone connector such as a headphone jack.

[0042] As used herein, the term "ray" generally refers to a line perpendicular to the wavefront of light, which points in the direction of the energy flow. As used herein, the term "beam" generally refers to a collection of rays. Hereinafter, the terms "ray" and "beam" will be used as synonyms. As further used herein, the term "light beam" generally refers to a quantity of light, specifically, a quantity of light traveling substantially in the same direction, including the possibility that the light beam has a spreading angle or a widening angle. The light beam can have a spatial extent. Specifically, the light beam can have a non-Gaussian beam profile. The beam profile can be selected from the group including the following: trapezoidal beam profile; triangular beam profile; conical beam profile. The trapezoidal beam profile can have a plateau region and at least one edge region. As will be outlined in more detail below, the light beam can specifically be a Gaussian beam or a linear combination of Gaussian beams. However, other embodiments are feasible. The delivery device can be configured to adjust, define, and determine one or more of the beam profiles (especially the shape of the beam profile).

[0043] As used herein, the term "object" refers to a point or region that emits at least one light beam (specifically, at least one reflection pattern). For example, the object can be at least one object selected from the group including the following: a scene, a human such as a person, wood, a carpet, foam, an animal such as a cow, a plant, a piece of tissue, metal, a toy, a metal object, a beverage, a food such as fruit, meat, fish, a plate, cosmetics, applied cosmetics, fabric, fur, hair, a maintenance product, a cream, an oil, a powder, a carpet, a juice, a suspension, paint, a plant, a body, a part of the body, an organic material, an inorganic material, a reflective material, a screen, a display, a wall, a piece of paper such as a photograph. The object can include at least one surface on which the illumination pattern is projected. The surface can be adapted to at least partially reflect the illumination pattern back to the detector. For example, without wishing to be bound by this theory, human skin can have a reflection profile, which is also referred to as a backscattering profile, including a portion generated by the back reflection of the surface (which is referred to as surface reflection), and a portion generated by the very diffuse reflection of the light that penetrates the skin (which is referred to as the diffuse reflection portion of the back reflection). Regarding the reflection profile of human skin, reference is made to "Lasertechnik in der Medizin: Grundlagen, Systeme, Anwendungen", "Wirkung von Laserstrahlung auf Gewebe", 1991, pages 171 to 266, Jürgen Eichler, Theo Seiler, Springer Verlag, ISBN 0939-0979. The surface reflection of the skin may increase as the wavelength increases towards the near infrared. In addition, the penetration depth can increase as the wavelength increases from visible light to near infrared. The diffuse reflection portion of the back reflection may increase with the penetration depth of the light. These material properties can be used to distinguish skin from other materials, specifically by analyzing the backscattering profile.

[0044] At least one light beam can propagate from the object towards the detector. The light beam can originate from the object or can originate from an illumination source, such as an illumination source that directly or indirectly illuminates the object, where the light beam is reflected or scattered by the object and is thus at least partially directed towards the detector. The detector can be used for active and / or passive illumination of the scene. For example, at least one illumination source can be adapted to illuminate the object, such as by directing a light beam towards the object, which reflects the light beam. As a supplement or alternative to at least one illumination source, the detector can use the radiation already present in the scene, such as radiation from at least one ambient light source.

[0045] The sensor element may be configured to record a beam profile of at least one reflection feature of a reflected image. The evaluation device may be configured to identify and / or select at least one reflection feature in the reflected image provided by the sensor element, specifically, at least one light spot. The evaluation device may be configured to perform at least one image analysis and / or image processing to identify the reflection feature. The image analysis and / or image processing may use at least one feature detection algorithm. The image analysis and / or image processing may include one or more of the following: filtering; selecting at least one region of interest; forming a difference image between an image generated from a sensor signal and at least one offset; inverting the sensor signal by inverting an image generated from the sensor signal; forming a difference image between images generated from sensor signals at different times; background correction; decomposition into color channels; decomposition into hue, saturation, and brightness channels; frequency decomposition; singular value decomposition; applying a blob detector; applying a corner detector; applying the determinant of a Hessian filter; applying a region detector based on principal curvature; applying a maximally stable extremal region detector; applying a generalized Hough transform; applying a ridge detector; applying an affine invariant feature detector; applying an affine-adapted interest point operator; applying a Harris affine region detector; applying a Hessian affine region detector; applying a scale-invariant feature transform; applying a scale-space extremum detector; applying a local feature detector; applying a speeded-up robust features algorithm; applying a histogram of oriented gradient positions and orientations algorithm; applying a histogram of oriented gradient descriptors; applying a Deriche edge detector; applying a differential edge detector; applying a spatio-temporal interest point detector; applying a Moravec corner detector; applying a Canny edge detector; applying the Laplacian of a Gaussian filter; applying a difference of Gaussian filter; applying a Sobel operator; applying a Laplacian operator; applying a Scharr operator; applying a Prewitt operator; applying a Roberts operator; applying a Kirsch operator; applying a high-pass filter; applying a low-pass filter; applying a Fourier transform; applying a Radon transform; applying a Hough transform; applying a wavelet transform; threshold conversion method; creating a binary image. Specifically, the evaluation of the reflected image includes selecting a region of interest in the reflected image. The region of interest may be determined manually by a user or may be determined automatically, such as by discerning an object within an image generated by the sensor element. For example, in the case of a punctiform reflection feature, the region of interest may be selected as the region around the light spot profile.

[0046] For example, the irradiation source may be adapted to generate and / or project a point cloud such that a plurality of irradiation regions are generated on the matrix of an optical sensor (e.g., a CMOS detector). Additionally, there may be interference on the matrix of the optical sensor, such as interference caused by spots and / or extraneous light and / or multiple reflections. The evaluation device may be adapted to determine at least one region of interest, such as one or more pixels irradiated by a light beam, which pixels are used to determine the ordinate of an object. For example, the evaluation device may be adapted to perform filtering methods, such as spot analysis and / or edge filters and / or object discrimination methods.

[0047] The evaluation device may be configured to perform at least one image correction. The image correction may include at least one background subtraction. The evaluation device may be adapted to remove the influence of background light from the reflected beam profile, for example, by imaging without further irradiation.

[0048] The evaluation device may be configured to determine a material property m by evaluating the beam profile of a reflected image. As used herein, the term "beam profile of a reflected image" refers to at least one intensity distribution of at least one reflection feature (such as a spot on a sensor element) in the reflection features of the reflected image, which is a function of pixels. The beam profile of a reflected image (also denoted as the reflected beam profile) may be selected from the group including: a trapezoidal beam profile; a triangular beam profile; a conical beam profile, and a linear combination of Gaussian beam profiles. As used herein, the term "evaluated beam profile" refers to applying at least one distance-related image filter and at least one material-related image filter to the beam profile and / or at least one specific region of the beam profile. As used herein, the term "image" refers to a two-dimensional function f(x,y), where the brightness and / or color value is given for any x, y position in the image. This position may be discretized corresponding to the recording pixels. The brightness and / or color may be discretized corresponding to the bit depth of the optical sensor. As used herein, the term "image filter" refers to at least one mathematical operation applied to the beam profile and / or at least one specific region of the beam profile. Specifically, the image filter Ф maps the image f or the region of interest in the image to a real number, where denotes a feature, in particular, the feature is a distance feature in the case of a distance-related image filter and a material feature in the case of a material-related image filter. The image may be subject to noise, and so may the feature. Thus, the feature may be a random variable. The feature may be normally distributed. If the feature is not normally distributed, they may be transformed to a normal distribution, such as by a Box-Cox transformation.

[0049] The evaluation device is configured to determine at least one distance feature by applying at least one distance-related image filter Ф1 to the reflected image As used herein, the term "distance" refers to the distance of an object, specifically the distance between the object and the detector. As used herein, the term "distance-related" image filter refers to an image having a distance-related output. The output of a distance-related image filter is herein referred to as a "distance feature" " or a "distance-related feature" ". A distance feature may be or may include at least one piece of information regarding the distance of the object, such as at least one measure of the distance of the object, a distance value, the ordinate of the object, etc. A distance-related image filter is at least one filter selected from the group consisting of: a photon depth ratio filter; a defocus depth filter; or a linear combination thereof; or is another distance-related image filter Ф 1其他 , which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, where Ф z is one of the photon depth ratio filter or the defocus depth filter or a linear combination thereof. Another distance-related image filter Ф 1其他 may be related to one or more of the distance-related image filters Ф Ф1其他,Фz by |ρ Ф1其他,Фz |≥0.60, preferably by |ρ z |≥0.80. The similarity of two image filters Ф i and Ф j can be evaluated by the correlation of their features, specifically, by calculating the Pearson correlation coefficient,

[0050]

[0051] where μ and σ are the mean and standard deviation of the obtained features. A set of random test images, specifically, a matrix filled with random numbers, can be used to perform the test of filter correlation. The number of random test images can be selected such that the result of the correlation test is statistically significant. The correlation coefficient takes values between -1 and 1, and 0 indicates no linear correlation. The correlation coefficient is very suitable for determining whether two filters are similar or even equivalent. To measure whether the features of a filter are related to a given property (such as distance), test images can be selected such that the relevant filter actually produces that property. As an example, to measure whether the features of a filter are related to distance, beam profiles recorded at different distances can be used as test images. To obtain a comparable, transferable, and transparent evaluation, a fixed test group of test images can be defined.

[0052] For example, the distance-related image filter can be a photon depth ratio filter. The photon depth ratio filter can include evaluating a combined signal Q of at least two sensor signals from a sensor element. The evaluation device can be configured to determine a distance feature by evaluating the combined signal Q The distance feature determined by evaluating the combined signal Q can directly correspond to the ordinate of the object. As used herein, "sensor signal" generally refers to a signal generated by an optical sensor and / or at least one pixel of an optical sensor in response to illumination. Specifically, the sensor signal can be or can include at least one electrical signal, such as at least one analog electrical signal and / or at least one digital electrical signal. More specifically, the sensor signal can be or can include at least one voltage signal and / or at least one current signal. More specifically, the sensor signal can include at least one photocurrent. Additionally, the original sensor signal can be used, or a detector, optical sensor, or any other element can be adapted to process or preprocess the sensor signal (such as preprocessing by filtering, etc.) to generate a secondary sensor signal, which can also be used as a sensor signal. As generally used herein, the term "combining" can generally refer to any operation in which two or more components (such as signals) are one or more of the following: mathematically combined to form at least one combined combined signal and / or compared to form at least one comparison signal or comparison result. As used herein, the term "combined signal Q" refers to a signal generated by combining sensor signals, specifically, by dividing sensor signals, dividing multiples in sensor signals, or dividing one or more of the linear combinations of sensor signals. In particular, the combined signal can be a quotient signal. The combined signal Q can be determined by using various devices. As an example, a software device for obtaining the combined signal, a hardware device for obtaining the combined signal, or both can be used and implemented in the evaluation device. Thus, as an example, the evaluation device can include at least one divider, where the divider is configured to obtain a quotient signal. The divider can be embodied in whole or in part as one or both of a software divider or a hardware divider.

[0053] The evaluation device can be configured to obtain the combined signal Q by dividing sensor signals, dividing multiples of sensor signals, dividing one or more of the linear combinations of sensor signals. The evaluation device can be configured to use the combined signal Q and the distance feature to determine the distance feature based on at least one predetermined relationship For example, the evaluation device is configured to obtain the combined signal Q by the following formula:

[0054]

[0055] where x and y are the abscissas, A1 and A2 are different areas of at least one beam profile at the sensor position of the light beam propagated from the object to the detector, and E(x, y, z o ) represents the object distance z o at a given beam profile. Regions A1 and A2 may be different. Specifically, A1 and A2 are not congruent. Thus, A1 and A2 may be different in one or more of shape or content. The beam profile may be a cross-section of the light beam. The beam profile may be selected from the group including the following: trapezoidal beam profile; triangular beam profile; linear combination of conical beam profile and Gaussian beam profile. Generally, the beam profile depends on the luminance L(z o ) and the beam shape S(x, y; z o ). Thus, by obtaining the combined signal, it is possible to determine the ordinate independently of the luminance. Additionally, using the combined signal allows the distance z o to be determined independently of the object size. Thus, the combined signal allows the distance z o to be determined independently of the material properties and / or reflection properties and / or scattering properties of the object and independently of changes in the light source (such as through manufacturing precision, heat, water, dirt, damage, etc. on the lens). As an example, the distance-related feature can be a function of the combined signal Q, and this function can be a linear, quadratic or higher-order polynomial in Q. Additionally, as an example, the object distance z0 can be a function of the distance-related feature , and this function can be a linear, quadratic or higher-order polynomial in

[0056] . Thus, the object distance z0 can be a function of the combined signal Q, z0 = z0(Q), and this function can be a linear, quadratic or higher-order polynomial in Q.

[0057] The evaluation device can be configured to determine and / or select a first region of the beam profile and a second region of the beam profile. The first region of the beam profile can include substantially edge information of the beam profile, and the second region of the beam profile can include substantially central information of the beam profile. The beam profile can have a center, i.e., the center point of the maximum value of the beam profile and / or the platform of the beam profile and / or the geometric center of the spot, and a falling edge extending from the center. The second region can include the inner region of the cross-section, while the first region can include the outer region of the cross-section. As used herein, the term "substantially central information" generally refers to a low proportion of edge information (i.e., the proportion of the intensity distribution corresponding to the edge) compared to the proportion of central information (i.e., the proportion of the intensity distribution corresponding to the center). Preferably, the central information has a proportion of edge information less than 10%, more preferably less than 5%, and most preferably, the central information does not include edge content. As used herein, the term "substantially edge information" generally refers to a low proportion of central information compared to the proportion of edge information. The edge information can include information of the entire beam profile, particularly from the center and edge regions. The edge information can have a proportion of central information less than 10%, preferably less than 5%, and more preferably, the edge information does not include central content. If at least one region of the beam profile is close to or around the center and includes substantially central information, then at least one region of the beam profile can be determined and / or selected as the second region of the beam profile. If at least one region of the beam profile includes at least a part of the falling edge of the cross-section, then at least one region of the beam profile can be determined and / or selected as the first region of the beam profile. For example, the entire region of the cross-section can be determined as the first region. The first region of the beam profile can be region A2, and the second region of the beam profile can be region A1.

[0058] The edge information can include information related to the number of photons in the first region of the beam profile, and the central information can include information related to the number of photons in the second region of the beam profile. The evaluation device can be adapted to determine the area integral of the beam profile. The evaluation device can be adapted to determine the edge information by integrating and / or summing the first region. The evaluation device can be adapted to determine the central information by integrating and / or summing the second region. For example, the beam profile can be a trapezoidal beam profile, and the evaluation device can be adapted to determine the integral of the trapezoid. In addition, when a trapezoidal beam profile can be assumed, the determination of the edge and central signals can be replaced by an equivalent evaluation that utilizes the characteristics of the trapezoidal beam profile, such as determining the slope and position of the edge and the height of the central platform, and deriving the edge and central signals through geometric considerations.

[0059] Additionally or alternatively, the evaluation device may be adapted to determine one or both of center information or edge information from at least one slice or incision of the light spot. For example, this can be achieved by replacing the area integral in the combined signal Q with a line integral along the slice or incision. To improve accuracy, several slices or incisions through the light spot may be used and averaged. In the case of an elliptical light spot profile, averaging over multiple slices or incisions may result in improved distance information.

[0060] The evaluation device may be configured to derive the combined signal Q by one or more of the following: dividing edge information and center information, dividing multiples of edge information and center information, dividing a linear combination of edge information and center information. Thus, basically, the photon ratio can be used as the physical basis of the method.

[0061] The evaluation device may specifically be configured to derive the combined signal Q by dividing the first and second sensor signals, dividing multiples of the first and second sensor signals, or dividing a linear combination of the first and second sensor signals. As an example, Q may simply be determined as Q = s1 / s2 or Q = s2 / s1, where s1 represents the first sensor signal and s2 represents the second sensor signal. Additionally or alternatively, Q may be determined as Q = a·s1 / b·s2 or Q = b·s2 / a·s1, where a and b are real numbers, which may be predetermined or determinable as an example. Additionally or alternatively, Q may be determined as Q = (a·s1 + b·s2) / (c·s1 + d·s2), where a, b, c, and d are real numbers, which are predetermined or determinable as an example. As a simple example of the latter, Q may be determined as Q = s1 / (s1 + s2). Other combined signals or quotient signals are also feasible.

[0062] Generally, the combined signal Q is a monotonic function of the ordinate of the object and / or the size of the light spot (such as the diameter or equivalent diameter of the light spot). Thus, as an example, specifically, in the case of using a linear optical sensor, the quotient Q = s1 / s2 is a monotonically decreasing function of the size of the light spot. Without wishing to be bound by this theory, it is believed that this is due to the fact that in the above setup, both the first signal s1 and the second signal s2 decrease as a square function with increasing distance from the light source because the amount of light reaching the detector decreases. However, therein, the first signal s1 decreases more rapidly than the second signal s2 because in the optical setup used in the experiment, the light spot in the image plane increases and thus is spread over a larger area. Therefore, the quotient of the first and second sensor signals continuously decreases with increasing diameter of the light beam or diameter of the light spot on the first and second photosensitive regions. Furthermore, the quotient is mainly independent of the total power of the light beam because the total power of the light beam forms a factor in both the first sensor signal and the second sensor signal. Thus, the combined signal Q can form a secondary signal that provides a unique and unambiguous relationship between the first and second sensor signals and the size or diameter of the light beam. On the other hand, since the size or diameter of the light beam depends on the distance between the object (from which the incident light beam propagates towards the detector) and the detector itself, i.e., depends on the ordinate of the object, there may be a unique and unambiguous relationship between the first and second sensor signals and the ordinate. For the latter, reference can be made, for example, to WO 2014 / 097181 A1. The predetermined relationship can be determined by analysis considerations (such as by assuming a linear combination of Gaussian beams), by empirical measurements (such as measurements of the first and second sensor signals or measurements of the secondary signal derived from the ordinate of the object), or both.

[0063] For further details and embodiments regarding the evaluation of the combined signal Q, reference can be made, for example, to WO 2018 / 091640, WO2018 / 091649A1 and WO 2018 / 091638 A2, the entire disclosures of which are incorporated herein by reference.

[0064] For example, the distance - related image filter can be a depth - of - focus filter. As outlined above, the evaluation device can be configured to determine at least one image of the region of interest from the sensor signals. The evaluation device can be configured to determine the distance characteristics of the object from the image by optimizing at least one blur function f a to determine the distance characteristics of the object from the image The determined distance characteristics can directly correspond to the ordinate of the object. The distance characteristics can be determined by using at least one convolution - based algorithm (such as the depth - of - focus algorithm). To obtain the distance from the image, the depth - of - focus algorithm estimates the defocus of the object. For this estimation, a blur function is assumed. As used herein, the term "blur function fa ”(also known as the blur kernel or point spread function) refers to the response function of the detector to the illumination from the object. Specifically, the blur function models the blur of the out-of-focus object. The at least one blur function f a can be a function or a composite function composed of at least one function from the group including the following: Gaussian function, sine function, pillbox function, square function, Lorentz function, radial function, polynomial, Hermite polynomial, Zernike polynomial, Legendre polynomial.

[0065] The blur function can be optimized by changing the parameters of the at least one blur function. The reflected image can be the blurred image i b . The evaluation device can be configured to reconstruct the distance feature from the blurred image i b and the blur function f a The distance feature can be determined by changing the parameter σ of the blur function, by minimizing the difference between the convolution of the blur function f a and at least one other image i’ b and the blurred image i b , min ‖(i′ b * f a (σ(z)) - i b )‖. σ(z) is a set of distance-related blur parameters. The other image may be blurred or clear. As used herein, the term "clear" or "clear image" refers to a blurred image with the maximum contrast. At least one other image can be generated from the blurred image i b by convolution with a known blur function. Therefore, the distance feature can be obtained using the defocus depth algorithm.

[0066] The evaluation device can be configured to determine at least one combined distance information z taking into account the distance feature determined by applying the photon depth ratio filter and the distance feature determined by applying the defocus depth filter. The combined distance information z can be a real function that depends on the distance feature determined by applying the photon depth ratio filter and the distance feature determined by applying the defocus depth filter. The combined distance information z can be the distance feature determined by applying the photon depth ratio filter and the distance feature determined by applying the defocus depth filter. ​Rational or irrational polynomials. The depth of defocus is a complementary method to the photon depth ratio, but uses a similar hardware setup. Additionally, the depth of defocus distance measurement may have a similar accuracy. Combining these two techniques can produce favorable distance measurement results with higher precision.

[0067] The evaluation device can be configured to determine at least one combined distance information using at least one recursive filter. The recursive filter can be at least one Kalman filter or at least one extended Kalman filter (EKF). The combined distance information z can be obtained using a real function z = f(z DPR , z DFD )(such as an arithmetic or geometric mean, a polynomial, preferably a polynomial up to the eighth order in z DPR and z DFD ), where z DPR is a distance feature determined by applying a photon depth ratio filter while z DFD is a distance feature determined by applying a depth of defocus filter The function f can be or can be based on a look-up table of pre-recorded values. For example, the evaluation device can include at least one data storage device configured to store pre-recorded values and / or one or more look-up tables. The function f can be based on a look-up table combined with an interpolation scheme for interpolating between the values in the look-up table. The interpolation scheme can be linear interpolation, spline interpolation, etc. In combination with a model or model function (such as the function f, involving the relationship between z, z DFD and z DPR ), z DFD and z DPR can be used as input variables within the recursive filter. The model or model function can include statistical hypotheses and / or statistical models involving the distances z, z DFD and z DPR , such as distributions, such as a Gaussian distribution of the measured distances z, z real around the actual distance z DFD and / or z DPR .

[0068] The recursive filter can be configured to determine combined distance information considering other sensor data and / or other parameters. The other parameters can include other information from sensor elements (such as CMOS sensors), such as information about the quality and / or noise of the recorded data and / or information about overexposure and / or information about underexposure, etc. The detector can include at least one other sensor configured to determine other sensor data. The recursive filter can be configured to determine combined distance information taking into account the other sensor data. The other sensor can be at least one sensor selected from the group including the following: temperature sensor, irradiation sensor (such as a control sensor for determining irradiation information), inertial measurement unit; gyroscope. The model and / or Kalman filter can include other input parameters, such as other sensor data, for example temperature and / or detector movement from the gyroscope and / or information from the inertial measurement unit, and / or information from the irradiation sensor, and / or other parameters, such as the quality / noise of the recorded data, overexposure, underexposure, etc. The other sensor data can be provided by sensor elements (in particular by at least one CMOS sensor and / or by further image analysis). The model and / or Kalman filter can include at least one material characteristic as an input variable.

[0069] For example, a distance-related image filter may be a structured light filter combined with a photon depth ratio filter and / or a defocus depth image filter. For example, the detector may include at least two sensor elements, each sensor element having a matrix of optical sensors. At least one first sensor element and at least one second sensor element may be located at different spatial positions. The relative distance between the first sensor element and the second element may be fixed. At least one first sensor element may be adapted to determine at least one first reflection pattern, specifically, at least one first reflection feature, and at least one second sensor element may be adapted to determine at least one second reflection pattern, specifically, at least one second reflection feature. The evaluation device may be configured to select at least one image determined by the first sensor element or the second sensor element as a reflection image, and be configured to select at least one image determined by the other sensor element of the first sensor element or the second sensor element as a reference image. As used herein, the term "reference image" refers to an image different from the reflection image, wherein the image is determined at a different spatial position compared to the reflection image. The reference image may be determined by one or more of recording at least one reference feature, imaging at least one reference feature, and calculating the reference image. The reference image and the reflection image may be object images determined at different spatial positions with a fixed distance. The distance may be a relative distance, also referred to as a baseline. The evaluation device may be adapted to select at least one reflection feature in the reflection image and determine at least one distance estimate of the selected reflection feature in the reflection image, the at least one distance estimate being determined by distance features determined by applying a photon depth ratio image filter and / or a defocus depth image filter and the error interval ±ε is given.

[0070] The evaluation device may be adapted to determine at least one reference feature corresponding to at least one reflection feature in at least one reference image. As outlined above, the evaluation device may be adapted to perform image analysis and identify features in the reflection image. The evaluation device may be adapted to identify at least one reference feature in the reference image that has a substantially identical ordinate to the selected reflection feature. The term "substantially identical" means identical within 10%, preferably 5%, and most preferably 1%. The reference feature corresponding to the reflection feature may be determined using epipolar geometry. For a description of epipolar geometry, for example, refer to chapter 2 in X. Jiang, H. Bunke: "Dreidimensionales Computersehen" Springer, Berlin Heidelberg, 1997. Epipolar geometry may assume that the reference image and the reflection image may be object images determined at different spatial positions and / or spatial orientations with a fixed distance. The evaluation device may be adapted to determine the epipolar line in the reference image. The relative position of the reference image and the reflection image may be known. For example, the relative position of the reference image and the reflection image may be stored in at least one storage unit of the evaluation device. The evaluation device may be adapted to determine a straight line extending from the selected reflection feature of the reflection image. The straight line may include possible object features corresponding to the selected feature. The straight line and the baseline span the epipolar plane. Since the reference image is determined at a different relative position from the reflection image, the corresponding possible object features may be imaged on the straight line (referred to as the epipolar line) in the reference image. Therefore, the feature of the reference image corresponding to the selected feature of the reflection image lies on the epipolar line. Due to image distortion or changes in system parameters, such as due to aging, temperature changes, mechanical stress, etc., the epipolar lines may intersect or be very close to each other and / or the correspondence between the reference feature and the reflection feature may be unclear. In addition, every known position or object in the real world can be projected onto the reference image and vice versa. Due to the calibration of the detector, the projection may be known, and the calibration is comparable to the teaching of the epipolar geometry of a specific camera.

[0071] The evaluation device may be configured to determine at least one displacement region corresponding to a distance estimate in a reference image. As used herein, the term "displacement region" refers to a region in the reference image in which a reference feature corresponding to a selected reflection feature can be imaged. Specifically, the displacement region may be a region in the reference image in which the reference feature corresponding to the selected reflection feature is expected to be located in the reference image. Depending on the distance to the object, the image position of the reference feature corresponding to the reflection feature may be shifted within the reference image compared to the image position of the reflection feature in the reflection image. The displacement region may include only one reference feature. The displacement region may also include more than one reference feature. The displacement region may include an epipolar line or a part of an epipolar line. The displacement region may include more than one epipolar line or multiple parts of more than one epipolar line. As used herein, the term "reference feature" refers to at least one feature of the reference image. The displacement region may extend along an epipolar line, be orthogonal to an epipolar line, or both. The evaluation device may be adapted to determine a reference feature along an epipolar line corresponding to a distance feature and determine a range of the displacement region along the epipolar line corresponding to an error interval ±ε or orthogonal to the epipolar line. The measurement uncertainty of the distance estimate may result in a non-circular displacement region because the measurement uncertainties in different directions may be different. Specifically, the measurement uncertainty along one or more epipolar lines may be greater than the measurement uncertainty in the direction orthogonal to one or more epipolar lines. The displacement region may include an extension in the direction orthogonal to one or more epipolar lines. The evaluation device may determine a displacement region around the image position of the reflection feature. The evaluation device may be adapted to determine a distance estimate and determine a displacement region along the epipolar line corresponding to the corresponding epipolar line.

[0072] The evaluation device may be configured to match selected features in the reflection pattern with at least one feature of the reference pattern within the displacement region. As used herein, the term "matching" refers to determining and / or evaluating corresponding reference and reflection features. The evaluation device may be configured to match selected features in the reflection image with reference features within the displacement region by using at least one evaluation algorithm that takes into account the determined distance estimate. The evaluation algorithm may be a linear scaling algorithm. The evaluation device may be adapted to determine the epipolar line closest to and / or within the displacement region. The evaluation device may be adapted to determine the epipolar line closest to the image position of the reflection feature. The extent of the displacement region along the epipolar line may be greater than the extent of the displacement region orthogonal to the epipolar line. The evaluation device may be adapted to determine the epipolar line before determining the corresponding reference feature. The evaluation device may determine a displacement region around the image position of each reflection feature. The evaluation device may be adapted to assign an epipolar line to each displacement region of each image position of the reflection feature, such as by assigning the epipolar line closest to and / or within the displacement region and / or closest to the displacement region along the direction orthogonal to the epipolar line. The evaluation device may be adapted to determine the reference feature corresponding to the image position of the reflection feature by determining the reference feature closest to and / or within the assigned displacement region and / or closest to the assigned displacement region along the assigned epipolar line and within the assigned displacement region along the assigned epipolar line.

[0073] The evaluation device may be configured to determine the displacement of the matched reference feature and the selected reflection feature. The evaluation device may be configured to use a predetermined relationship between the ordinate and the displacement to determine the longitudinal information of the matched feature. As used herein, the term "displacement" refers to the difference between the position in the reference image and the position in the reflection image. As used herein, the term "longitudinal information" refers to information related to the ordinate. For example, the longitudinal information may be a distance value. The predetermined relationship may be one or more of an empirical relationship, a semi-empirical relationship, and a relationship obtained by analysis. The evaluation device may include at least one data storage device for storing the predetermined relationship (such as a look-up list or a look-up table). The evaluation device may be adapted to determine the predetermined relationship by using a triangulation method. In the case where the position of the selected reflection feature in the known reflection image and the position of the matched reference feature and / or the relative displacement between the selected reflection feature and the matched reference feature are known, the ordinate of the corresponding object feature may be determined by triangulation. Thus, the evaluation device may be adapted to select, for example, subsequent and / or column-by-column reflection features and use triangulation to determine the corresponding distance value for each potential position of the reference feature. The displacement and the corresponding distance value may be stored in at least one storage device of the evaluation device.

[0074] Additionally or alternatively, the evaluation device may be configured to perform the following steps:

[0075] - Determine a displacement region for the image position of each reflection feature;

[0076] - Assign epipolar lines to the displacement regions of each reflection feature, such as by assigning epipolar lines closest to and / or within the displacement region and / or along the epipolar line orthogonal to the epipolar line and closest to the displacement region;

[0077] - Assign and / or determine at least one reference feature to each reflection feature, such as by assigning reference features closest to and / or within the assigned displacement region and / or along the assigned epipolar line and closest to the assigned displacement region and / or within the assigned displacement region along the assigned epipolar line.

[0078] Additionally or alternatively, the evaluation device may be adapted to make a decision between reference features and / or more than one epipolar line to be assigned to a reflection feature, such as by comparing distances of epipolar lines and / or reflection features within a reference image and / or by comparing error-weighted distances (such as ε-weighted distances of epipolar lines and / or reflection features within a reference image) and assigning reference features and / or epipolar lines with shorter distances and / or ε-weighted distances.

[0079] The evaluation device is configured to determine at least one material feature by applying at least one material-related image filter Ф2 to the reflection image As used herein, the term "material-related" image filter refers to an image having a material-related output. The output of a material-related image filter is denoted herein as "material feature " or "material-related feature ". The material feature may be or may include at least one piece of information about at least one material property of the object.

[0080] The material-related image filter may be at least one filter selected from the group including the following: a luminance filter; a spot shape filter; a squared norm gradient; a standard deviation; a smoothing filter, such as a Gaussian filter or a median filter; a contrast filter based on grey-level-occurrence; an energy filter based on grey-level-occurrence; a homogeneity filter based on grey-level-occurrence; a dissimilarity filter based on grey-level-occurrence; Law's energy filter; a threshold region filter; or a linear combination thereof; or another material-related image filter Ф 2其他 , which by |ρ Ф2其他,Фmis related to one or more of a luminance filter; a spot shape filter; a squared norm gradient; a standard deviation; a smoothing filter; an energy filter based on gray level occurrence; a homogeneity filter based on gray level occurrence; a dissimilarity filter based on gray level occurrence; a Lowe energy filter; or a threshold region filter; or a linear combination thereof, wherein Ф m is one of a luminance filter; a spot shape filter; a squared norm gradient; a standard deviation; a smoothing filter; an energy filter based on gray level occurrence; a homogeneity filter based on gray level occurrence; a dissimilarity filter based on gray level occurrence; a Lowe energy filter; or a threshold region filter; or a linear combination thereof. Another material-related image filter Ф 2其他 is related to one or more of Ф Ф2其他,Фm by |ρ Ф2其他,Фm |≥0.60, preferably by |ρ m |≥0.80.

[0081] The material-related image filter can be at least one arbitrary filter Φ by hypothesis testing. As used herein, the term "by hypothesis testing" refers to the fact that the null hypothesis H0 is rejected and the alternative hypothesis H1 is accepted. Hypothesis testing can include testing the material correlation of an image filter by applying the image filter to a predefined data set. The data set can include a plurality of beam profile images. As used herein, the term "beam profile image" refers to the sum of N B Gaussian radial basis functions,

[0082]

[0083] wherein each of the N B Gaussian radial basis functions is defined by a center (x lk , y lk ), a prefactor a lk and an exponential factor α = 1 / ∈. The exponential factors of all Gaussian functions in all images are the same. The center positions x k : of all images f lk , y lk are the same. Each beam profile image in the beam profile images of the data set can correspond to a material classifier and a distance. The material classifier can be a label such as "Material A", "Material B", etc. The above formula of f k (x, y) can be used in combination with the following parameter table to generate a beam profile image:

[0084]

[0085] The values of x, y are corresponding to having The integer of the pixel. The image can have a pixel size of 32x32. By using the above f in combination with the parameter set k of the formula to obtain the continuous description of f k to generate a data set of beam profile images. The value of each pixel in the 32x32 image can be obtained by inserting integer values from 0 to 31 for x and y in f k (x,y). For example, for the pixel (6,9), the value f k (6,9) can be calculated.

[0086] Subsequently, for each image f k , the eigenvalue corresponding to the filter Φ can be calculated where z k is the distance value corresponding to the image f k from the predefined data set. This produces a data set with the corresponding generated eigenvalues . Hypothesis testing can use the null hypothesis that the filter does not distinguish between material classifiers. The null hypothesis can be given by H0: μ1 = μ2 = … = μ J where μ m is the expected value of each material group corresponding to the eigenvalue . The index m represents the material group. Hypothesis testing can use the alternative hypothesis that the filter does indeed distinguish between at least two material classifiers. The alternative hypothesis can be given by H1: μ m ≠μ m′ . As used herein, the term "does not distinguish between material classifiers" means that the expected values of the material classifiers are the same. As used herein, the term "distinguishes between material classifiers" means that at least two of the expected values of the material classifiers are different. As used herein, "distinguishes between at least two material classifiers" is used synonymously with "suitable material classifier". Hypothesis testing can include at least one analysis of variance (ANOVA) on the generated eigenvalues. In particular, hypothesis testing can include determining the mean of the eigenvalues for each of the J materials, i.e., a total of J means, where m ∈ [0, 1, …, J - 1], where N m gives the number of eigenvalues for each of the J materials in the predefined data set. Hypothesis testing can include determining the mean of all N eigenvalues. Hypothesis testing can include determining the sum of mean squares within the following range:

[0087]

[0088] Hypothesis testing can include determining the sum of mean squares between the following,

[0089]

[0090] Hypothesis testing may include performing an F-test:

[0091] □ where d1 = N - J, d2 = J - 1,

[0092] □F(x) = 1 - CDF(x)

[0093] □p = F(mssb / mssw)

[0094] In this article, I x is the regularized incomplete beta function (Beta-Function), where the Euler beta function and are the incomplete beta functions. If the p-value p is less than or equal to a predefined significance level, the image filter can pass the hypothesis test. If p ≤ 0.075, preferably p ≤ 0.05, more preferably p ≤ 0.025, and most preferably p ≤ 0.01, then the filter can pass the hypothesis test. For example, in the case where the predefined significance level is α = 0.075, if the p-value is less than α = 0.075, the image filter can pass the hypothesis test. In this case, the null hypothesis H0 can be rejected, and the alternative hypothesis H1 can be accepted. The image filter thus distinguishes at least two material classifiers. Therefore, the image filter passes the hypothesis test.

[0095] Below, it is assumed that the reflected image includes at least one reflection feature, specifically, a spot image, to describe the image filter. The spot image f can be given by the function f: given, where the background of the image f may have been subtracted. However, other reflection features are also possible.

[0096] For example, the material-related image filter can be a luminance filter. The luminance filter can return a luminance measure of the spot as a material feature. The material feature can be determined by

[0097]

[0098] where f is the spot image. The distance of the spot is represented by z, where z can be obtained, for example, by using defocus depth or photon depth ratio techniques and / or by using triangulation techniques. The surface normal of the material is given by given, and can be obtained as the normal of the surface spanned by at least three measurement points. The vector is the direction vector of the light source. Since the position of the spot is known by using defocus depth or photon depth ratio techniques and / or by using triangulation techniques, where the position of the light source is known as a parameter of the detector system, d 光线is the differential vector between the light spot and the light source position.

[0099] For example, the material-related image filter can be a filter with an output that depends on the shape of the light spot. The material-related image filter can return a value related to the translucency of the material as a material feature. The translucency of the material affects the shape of the light spot. The material feature can be given by

[0100]

[0101] where 0 < α, β < 1 are weights for the light spot height h, and H represents the Heavyside function, i.e., H(x) = 1 for x ≥ 0 and H(x) = 0 for x < 0. The light spot height h can be determined by

[0102]

[0103] where B r is the inner circle of the light spot with radius r.

[0104] For example, the material-related image filter can be the squared norm gradient. The material-related image filter can return a value related to a measure of the soft and hard transitions and / or roughness of the light spot as a material feature. The material feature can be defined by

[0105]

[0106] For example, the material-related image filter can be the standard deviation. The standard deviation of the light spot can be determined by

[0107]

[0108] where μ is the average value given by μ = ∫(f(x))dx.

[0109] For example, the material-related image filter can be a smoothing filter such as a Gaussian filter or a median filter. In one embodiment of the smoothing filter, the image filter can refer to the observation that volume scattering exhibits less speckle contrast compared to diffusive materials. The image filter can quantify the smoothness of the light spot corresponding to the speckle contrast as a material feature. The material feature can be determined by

[0110]

[0111] where is a smoothness function, such as a median filter or a Gaussian filter. The image filter may include dividing by the distance z, as described in the above equation. The distance z can be determined, for example, using defocus depth or photon depth ratio techniques and / or by using triangulation techniques. This allows the filter to be insensitive to distance. In one embodiment of the smoothing filter, the smoothing filter may be based on the standard deviation of the extracted speckle noise pattern. The speckle noise pattern N can be empirically described by the following equation

[0112] f(x) = f0(x) · (N(X) + 1),

[0113] where f0 is the image of the despeckled spot. N(X) is the noise term that models the speckle pattern. Calculating the despeckled image can be difficult. Therefore, the despeckled image can be approximated by a smoothed version of f, i.e., where is a smoothing operator similar to a Gaussian filter or a median filter. Therefore, the approximation of the speckle pattern can be given by the following equation

[0114]

[0115] The material characteristics of the filter can be determined by the following equation

[0116]

[0117] where Var represents the variance function.

[0118] For example, the image filter can be a contrast filter based on gray-level occurrence. The material filter can be based on the gray-level occurrence matrix M f,ρ (g1g2) = [p g1,g2 , and p g1,g2 is the occurrence rate of the gray-level combination (g1, g2) = [f(x1, y1), f(x2, y2)], and the relationship ρ defines the distance between (x1, y1) and (x2, y2), which is ρ(x,y) = (x + a, y + b), where a and b are selected from 0, 1.

[0119] The material characteristics of the contrast filter based on gray-level occurrence can be given by the following equation

[0120]

[0121] For example, the image filter can be an energy filter based on gray-level occurrence. The material filter is based on the gray-level occurrence matrix defined above.

[0122] The material characteristics of the energy filter based on gray-level occurrence can be given by the following equation

[0123]

[0124] For example, the image filter can be a homogeneity filter based on gray-level occurrence. This material filter is based on the gray-level occurrence matrix defined above.

[0125] The material characteristics of the homogeneity filter based on gray-level occurrence can be given by the following formula

[0126]

[0127] For example, the image filter can be a dissimilarity filter based on gray-level occurrence. This material filter is based on the gray-level occurrence matrix defined above.

[0128] The material characteristics of the dissimilarity filter based on gray-level occurrence can be given by the following formula

[0129]

[0130] For example, the image filter may be a Lowe's energy filter. This material filter can be based on the Lowe's vectors L5 = [1, 4, 6, 4, 1] and E5 = [-1, -2, 0, -2, -1] and the matrices L5(E5) T and E5(L5) T .

[0131] The image f k is convolved with these matrices:

[0132]

[0133] and

[0134]

[0135] and the material characteristics of the Lowe's energy filter can be determined by the following formula

[0136]

[0137] For example, the material-related image filter can be a threshold region filter. The material characteristics may involve two regions in the image plane. The first region Ω1 can be the region where the function f is greater than α times the maximum value of f. The second region Ω2 can be the region where the function f is less than α times the maximum value of f but greater than the threshold ε times the maximum value of f. Preferably, α can be 0.5 and ε can be 0.05. Due to speckle or noise, these regions may not simply correspond to the inner and outer circles around the center of the light spot. As an example, Ω1 can include speckles or unconnected regions in the outer circle. The material characteristics can be determined by the following formula

[0138]

[0139] where Ω1 = {x|f(x) > α·max(f(x))} and Ω2 = {x|ε·max(f(x)) < f(x) < α·max(f(x))}.

[0140] The material property m and / or the ordinate z can be determined by using a predefined relationship between z and m. The evaluation device can be configured to determine the material property m and / or the ordinate z by evaluating features For example, the evaluation device can be configured to use at least one predefined relationship between a distance feature and the ordinate of the object to determine the ordinate z of the object. The evaluation device can be configured to use a material feature and at least one predefined relationship between the material property of the object to determine the material property of the object. The predefined relationship can be one or more of an empirical relationship, a semi-empirical relationship, and a relationship obtained by analysis. The evaluation device can include at least one data storage device for storing the predefined relationship (such as a lookup list or a lookup table). For example, the material property can be determined by subsequently evaluating after determining the ordinate z, such that information about the ordinate z can be taken into account for the evaluation Specifically, the material property m and / or the ordinate z can be determined by a function and / or a function The function can be predefined and / or predetermined. For example, the function can be a linear function.

[0141] Ideally, the image filter would produce features that depend only on the distance or the material property. However, the image filters used in beam profile analysis may produce features that depend on both the distance and the material property, such as translucency. At least one of the material-dependent image filter or the distance-dependent image filter can be a function of the features or of at least one other image filter. At least one of the material-dependent image filter or the distance-dependent image filter can be a function of at least one other image filter. The evaluation device can be configured to determine whether at least one of Ф1 or Ф2 is a feature of another image filter or functions, and / or determining whether at least one of Ф1 or Ф2 is a function of at least one other image filter. Specifically, the evaluation device may be configured to determine the correlation coefficient between the material-related image filter and the distance-related image filter. In the case where the correlation coefficient between the material-related image filter and the distance-related image filter is close to 1 or -1, the distance can be projected by projecting the material features onto the principal axis with the lowest variance. As an example, the material features can be projected onto an axis orthogonal to the relevant principal component. In other words, the material features can be projected onto the second principal component. This can be done using principal component analysis known to those skilled in the art. The evaluation device may be configured to apply the distance-related image filter and the material-related image filter to the reflected image simultaneously. In particular, the evaluation device may be configured to simultaneously determine features that are strongly correlated with the distance and weakly correlated with the material properties, and features that are weakly correlated with the distance and strongly correlated with the material properties. Alternatively, the evaluation device may be configured to apply the distance-related image filter and the material-related image filter to the reflected image sequentially or recursively. The evaluation device may be configured to determine at least one of z and / or m by applying at least one other filter that depends on at least one of and to the reflected image.

[0142] As further used herein, the term "evaluation device" generally refers to any device suitable for performing the specified operations, preferably by using at least one data processing device, and more preferably, by using at least one processor and / or at least one application-specific integrated circuit. Thus, as an example, at least one evaluation device may include at least one data processing device having software code stored thereon, the software code including a plurality of computer commands. The evaluation device may provide one or more hardware elements for performing one or more specified operations, and / or may provide software running thereon to one or more processors for performing one or more specified operations. The above operations including the analysis of the beam profile are performed by at least one evaluation device. Thus, as an example, one or more of the above relationships may be implemented in software and / or hardware, such as by implementing one or more look-up tables. Thus, as an example, the evaluation device may include one or more programmable devices, such as one or more computers, application-specific integrated circuits (ASICs), digital signal processors (DSPs), or field-programmable gate arrays (FPGAs), which are configured to perform the above evaluation. However, additionally or alternatively, the evaluation device may also be embodied entirely or partially in hardware.

[0143] The detector may further include one or more additional elements, such as one or more additional optical elements. In addition, the detector may be fully or partially integrated into at least one housing.

[0144] The detector may include at least one optical element selected from the group consisting of: a delivery device, such as at least one lens and / or at least one lens system, at least one diffractive optical element. The term "delivery device" (also referred to as "delivery system") generally may refer to one or more optical elements that are adapted to modify a light beam, such as by modifying one or more of the beam parameters of the light beam, the width of the light beam, or the direction of the light beam. The delivery device may be adapted to direct the light beam onto an optical sensor. The delivery device may specifically include one or more of the following: at least one lens, such as at least one lens selected from the group consisting of: at least one focus-adjustable lens, at least one aspherical lens, at least one spherical lens, at least one Fresnel lens; at least one diffractive optical element; at least one concave mirror; at least one beam deflection element, preferably at least one mirror; at least one beam splitting element, preferably at least one of a beam splitting cube or a beam splitting mirror; at least one multi-lens system. As used herein, the "focal length" of the delivery device refers to the distance at which an incident collimated light ray that may impinge on the delivery device enters the "focus", which may also be referred to as the "focal point". Thus, the focal length constitutes a measure of the ability of the delivery device to converge the impinging light beam. Thus, the delivery device may include one or more imaging elements that may have a converging lens action. By way of example, the delivery device may have one or more lenses, particularly one or more refractive lenses, and / or one or more convex mirrors. In this example, the focal length may be defined as the distance from the center of a thin refractive lens to the principal focus of the thin lens. For a converging thin refractive lens (such as a convex or biconvex thin lens), the focal length may be considered positive and may provide a distance at which a collimated light beam impinging on the thin lens as the delivery device may be focused into a single spot. Additionally, the delivery device may include at least one wavelength selection element, such as at least one filter. Additionally, the delivery device may be designed to (e.g., at the location of the sensor area, and particularly in the sensor area) impose a predetermined beam profile on the electromagnetic radiation. In principle, the above-described alternative embodiments of the delivery device may be implemented individually or in any desired combination.

[0145] The transfer device may have an optical axis. In particular, the detector and the transfer device have a common optical axis. As used herein, the term "optical axis of the transfer device" generally refers to the axis of mirror symmetry or rotational symmetry of a lens or lens system. The optical axis of the detector may be the symmetry line of the optical means of the detector. The detector includes at least one transfer device, preferably at least one transfer system having at least one lens. As an example, the transfer system may include at least one beam path, where the elements of the transfer system in the beam path are positioned in a rotationally symmetric manner about the optical axis. Still, as will also be outlined in more detail below, one or more optical elements located within the beam path may also be eccentric or tilted with respect to the optical axis. However, in this case, the optical axis may be defined sequentially, such as by interconnecting the centers of the optical elements in the beam path, for example by interconnecting the centers of the lenses, where, in this case, the optical sensor is not considered an optical element. The optical axis may generally represent the beam path. Wherein the detector may have a single beam path along which the light beam may travel from the object to the optical sensor, or may have multiple beam paths. As an example, a single beam path may be given, or the beam path may be divided into two or more partial beam paths. In the latter case, each partial beam path may have its own optical axis. The optical sensor may be located in one and the same beam path or partial beam path. Alternatively, however, the optical sensor may also be located in different partial beam paths.

[0146] The transfer device may form a coordinate system, where the ordinate l is the coordinate along the optical axis, and where d is the spatial offset from the optical axis. The coordinate system may be a polar coordinate system, where the optical axis of the transfer device forms the z-axis, and where the distance from the z-axis and the polar angle may be used as additional coordinates. A direction parallel or antiparallel to the z-axis may be considered the longitudinal direction, and the coordinate along the z-axis may be considered the ordinate l. Any direction perpendicular to the z-axis may be considered the transverse direction, and the polar coordinates and / or the polar angle may be considered the abscissa.

[0147] As outlined above, the detector can be capable of determining at least one ordinate of an object, including options for determining the ordinate of the entire object or one or more of its parts. For example, the detector can be configured to determine the ordinate of an object by using at least one distance-related filter and using the photon depth ratio technique and / or the defocus depth technique outlined above. The detector can be configured to determine the position of the object. As used herein, the term "position" refers to at least one piece of information regarding the position and / or orientation of the object and / or at least a part of the object in space. A distance can be an ordinate or can contribute to determining the ordinate of a point of the object. Additionally or alternatively, one or more other pieces of information regarding the position and / or orientation of the object and / or at least a part of the object can be determined. As an example, additionally, at least one abscissa of the object and / or at least a part of the object can be determined. Thus, the position of the object can imply at least one ordinate of the object and / or at least a part of the object. Additionally or alternatively, the position of the object can imply at least one abscissa of the object and / or at least a part of the object. Additionally or alternatively, the position of the object can imply at least one orientation information of the object, which indicates the orientation of the object in space.

[0148] However, in addition, other coordinates of the object, including one or more abscissa coordinates and / or rotation coordinates, can be determined by the detector, specifically by the evaluation means. Thus, by way of example, one or more lateral sensors can be used to determine at least one abscissa coordinate of the object. At least one of the optical sensors can be determined from which a center signal is generated. This can provide information about at least one abscissa coordinate of the object, where, by way of example, a simple lens equation can be used for optical conversion and for deriving the abscissa. Additionally or alternatively, one or more additional lateral sensors can be used and can be included by the detector. Various lateral sensors are generally known in the art, such as the lateral sensors disclosed in WO 2014 / 097181 A1 and / or other position-sensitive devices (PSD), such as quadrant diodes, CCDs or CMOS chips, etc. Additionally or alternatively, by way of example, the detector according to the invention can include one or more PSDs disclosed in R.A. Street (Ed.): Technology and Applications of Amorphous Silicon, Springer-Verlag Heidelberg, 2010, pp. 346 - 349. Other embodiments are feasible. These devices can generally also be implemented in the detector according to the invention. By way of example, a part of the light beam can be separated by at least one beam-splitting element within the detector. By way of example, the separated part can be directed to a lateral sensor (such as a CCD or CMOS chip or camera sensor), and the lateral position of the light spot generated by the separated part on the lateral sensor can be determined, thereby determining at least one abscissa coordinate of the object. Thus, the detector according to the invention can be a one-dimensional detector, such as a simple distance measurement device, or can be embodied as a two-dimensional detector or even as a three-dimensional detector. Furthermore, as outlined above or as outlined in further detail below, a three-dimensional image can also be created by scanning the scene or environment in a one-dimensional manner. Thus, the detector according to the invention can specifically be one of a one-dimensional detector, a two-dimensional detector or a three-dimensional detector. The evaluation means can further be configured to determine at least one abscissa coordinate x, y of the object. The evaluation means can be adapted to combine the information of the ordinate and abscissa coordinates and determine the position of the object in space.

[0149] The use of a matrix of optical sensors offers several advantages and benefits. Thus, the center of the light spot generated by a light beam on the sensor elements (such as on the common plane of the photosensitive areas of the optical sensors of the matrix of sensor elements) can vary with the lateral position of the object. Thus, the use of a matrix of optical sensors provides significant flexibility in terms of the position of the object, specifically in terms of the lateral position of the object. The lateral position of the light spot on the matrix of optical sensors (such as the lateral position of at least one optical sensor generating a sensor signal) can be used as an additional information item from which at least one piece of information about the lateral position of the object can be derived, as disclosed, for example, in WO 2014 / 198629 A1. Additionally or alternatively, the detector according to the invention can comprise at least one additional lateral detector for detecting at least one abscissa of the object in addition to at least one ordinate.

[0150] The invention discloses in other aspects a detector for identifying at least one material property m, comprising:

[0151] - at least one sensor element comprising a matrix of optical sensors, each optical sensor having a photosensitive area, wherein the sensor element is configured to record at least one reflected image of a light beam originating from at least one object;

[0152] - at least one evaluation device configured to determine the material property by evaluating at least one beam profile of the reflected image,

[0153] wherein the evaluation device is configured to determine at least one distance feature by applying at least one distance-dependent image filter Ф1 to the reflected image wherein the distance-dependent image filter is at least one filter selected from the group comprising: a photon depth ratio filter; a defocus depth filter; or a linear combination thereof; or is another distance-dependent image filter Ф 1其他 , which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, wherein Ф z is one of the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof,

[0154] wherein the evaluation device is configured to determine at least one material feature by applying at least one material-dependent image filter Ф2 to the reflected image Among them, the material-related image filter is at least one filter selected from the group including the following: luminance filter; spot shape filter; squared norm gradient; standard deviation; smoothing filter, such as Gaussian filter or median filter; contrast filter based on gray level occurrence; energy filter based on gray level occurrence; homogeneity filter based on gray level occurrence; dissimilarity filter based on gray level occurrence; Lowe's energy filter; threshold region filter; or a linear combination thereof; or another material-related image filter Ф 2其他 which is related to a luminance filter; spot shape filter; squared norm gradient; standard deviation; smoothing filter; energy filter based on gray level occurrence; homogeneity filter based on gray level occurrence; dissimilarity filter based on gray level occurrence; Lowe's energy filter; or threshold region filter; or a linear combination thereof by |ρ Ф2其他,Фm |≥0.40, where Ф m is one of a luminance filter; spot shape filter; squared norm gradient; standard deviation; smoothing filter; energy filter based on gray level occurrence; homogeneity filter based on gray level occurrence; dissimilarity filter based on gray level occurrence; Lowe's energy filter; or threshold region filter; or a linear combination thereof,

[0155] Among them, the evaluation device is configured to determine the ordinate z and the material property m by evaluating the distance feature and the material feature to determine the ordinate z and the material property m.

[0156] Regarding the definitions and embodiments, reference is made to the description of the detector described in the first aspect of the present invention.

[0157] The present invention discloses in other aspects a detector for identifying at least one material property m, comprising:

[0158] - at least one sensor element, which includes a matrix of optical sensors, each optical sensor having a photosensitive area, wherein the sensor element is configured to record at least one reflected image of a light beam originating from at least one object;

[0159] - at least one evaluation device, which is configured to determine the material property by evaluating at least one beam profile of the reflected image,

[0160] wherein the evaluation device is configured to determine at least one distance feature by applying at least one distance-related image filter Ф1 to the reflected image wherein the distance-related image filter is at least one filter selected from the group including the following: photon depth ratio filter; defocus depth filter; or a linear combination thereof; or another distance-related image filter Ф 1其他 which is related to a luminance filter; spot shape filter; squared norm gradient; standard deviation; smoothing filter; energy filter based on gray level occurrence; homogeneity filter based on gray level occurrence; dissimilarity filter based on gray level occurrence; Lowe's energy filter; or threshold region filter; or a linear combination thereof by |ρФ1其他,Фz |≥0.40 is associated with a photon depth ratio filter and / or a defocus depth filter or a linear combination thereof, where Ф z is one of a photon depth ratio filter and / or a defocus depth filter or a linear combination thereof,

[0161] wherein the evaluation device is configured to determine at least one material feature by applying at least one material-related image filter Ф2 to the reflected image wherein the material-related image filter is at least one filter by means of a hypothesis test, wherein the hypothesis test uses a null hypothesis that the filter does not distinguish material classifiers and an alternative hypothesis that the filter distinguishes at least two material classifiers, and wherein the filter passes the hypothesis test if the p-value p is less than or equal to a predefined significance level,

[0162] wherein the evaluation device is configured to determine the ordinate z and the material property m by evaluating the distance feature and the material feature to determine the ordinate z and the material property m.

[0163] For definitions and examples, reference is made to the description of the detector described in the first aspect of the present invention.

[0164] In other aspects of the present invention, a detector system is disclosed. The detector system includes at least one detector according to the present invention (such as according to one or more of the embodiments disclosed above or according to one or more of the embodiments disclosed in further detail below). The detector system further includes at least one beacon device adapted to direct at least one light beam towards the detector, wherein the beacon device is at least one of attachable to an object, graspable by an object, and integratable into an object. Further details regarding the beacon device, including its potential embodiments, will be given below. Thus, the at least one beacon device may be or may include at least one active beacon device, which includes one or more illumination sources, such as one or more light sources, such as lasers, LEDs, light bulbs, etc. As an example, the light emitted by the illumination source may have a wavelength of 300 to 500 nm. Alternatively, as outlined above, the infrared spectral range may be used, such as in the range of 780 nm to 3.0 μm. Specifically, the near-infrared region where silicon photodiodes are specifically applicable in the range of 700 nm to 1000 nm may be used. As outlined above, the light emitted by one or more beacon devices may be unmodulated or may be modulated in order to distinguish between two or more light beams. Additionally or alternatively, at least one beacon device may be adapted to reflect one or more light beams towards the detector, such as by including one or more reflective elements. Furthermore, the at least one beacon device may be or may include one or more scattering elements adapted to scatter the light beam. Among them, elastic or inelastic scattering may be used. In the case where at least one beacon device is adapted to reflect and / or scatter the main light beam towards the detector, the beacon device may be adapted to leave the spectral characteristics of the light beam unaffected, or alternatively may be adapted to change the spectral characteristics of the light beam, such as by modifying the wavelength of the light beam.

[0165] In other aspects of the present invention, a human-machine interface for exchanging at least one piece of information between a user and a machine is disclosed. The human-machine interface includes at least one detector system according to one or more of the embodiments disclosed above and / or according to one or more of the embodiments disclosed in further detail below. Among them, at least one beacon device is adapted to be attached to the user or grasped by the user in at least one of directly or indirectly. The human-machine interface is designed to determine at least one position of the user by means of the detector system, wherein the human-machine interface is designed to assign at least one piece of information to this position.

[0166] In other aspects of the present invention, an entertainment device for performing at least one entertainment function is disclosed. The entertainment device includes at least one human-machine interface according to one or more of the embodiments disclosed above and / or according to one or more of the embodiments disclosed in further detail below. The entertainment device is configured such that a player can input at least one piece of information by means of the human-machine interface. The entertainment device is further configured to change the entertainment function according to the information.

[0167] In other aspects of the present invention, a tracking system for tracking the position of at least one movable object is disclosed. The tracking system includes at least one detector system according to one or more embodiments of the detector system as disclosed above and / or as further detailed below. The tracking system further includes at least one tracking controller. The tracking controller is adapted to track a series of positions of the object at a particular point in time.

[0168] In other aspects of the present invention, a camera for imaging at least one object is disclosed. The camera includes at least one detector according to any one of the embodiments of the detector as disclosed above or as further detailed below.

[0169] In other aspects of the present invention, a scanning system for determining the depth profile of a scene is provided, which may also imply determining at least one position of at least one object. The scanning system includes at least one detector according to the present invention, such as at least one detector disclosed in one or more of the embodiments listed above and / or in one or more of the embodiments below. The scanning system further includes at least one illumination source adapted to scan the scene with at least one light beam, which may also be referred to as an illumination beam or a scanning beam. As used herein, the term "scene" generally refers to a two-dimensional or three-dimensional range visible to the detector such that at least one geometric or spatial property of the two-dimensional or three-dimensional range can be evaluated with the detector. As further used herein, the term "scanning" generally refers to consecutive measurements in different regions. Thus, scanning may specifically imply at least one first measurement in which the illumination beam is oriented or directed in a first manner, and at least one second measurement in which the illumination beam is oriented or directed in a second manner different from the first manner. The scanning may be continuous scanning or stepwise scanning. Thus, in a continuous or stepwise manner, the illumination beam may be directed to different regions of the scene, and the detector may be detected to generate at least one piece of information for each region, such as at least one ordinate. As an example, in order to scan an object, one or more illumination beams may continuously or stepwise produce light spots on the surface of the object, where the ordinate of the light spot is generated. However, alternatively, a light pattern may be used for scanning. The scanning may be point scanning or line scanning, or even scanning with a more complex light pattern. The illumination source of the scanning system may be different from the optional illumination source of the detector. However, alternatively, the illumination source of the scanning system may also be completely or partially the same as or integrated into at least one optional illumination source of the detector.

[0170] Accordingly, the scanning system may include at least one illumination source adapted to emit at least one light beam configured to illuminate at least one point at at least one surface of at least one object. As used herein, the term "point" refers to a region on a portion of the object surface, specifically a small region, which may be selected, for example, by a user of the scanning system to be illuminated by the illumination source. Preferably, on the one hand, the point may exhibit as small a size as possible to allow the scanning system to determine the value of the distance between the illumination source included in the scanning system and the portion of the object surface (where the point may be positioned as precisely as possible), and on the other hand, it may be as large as possible to allow the user of the scanning system or the scanning system itself, particularly through an automatic program, to detect the presence of a point on the relevant portion of the object surface.

[0171] To this end, the illumination source may include an artificial illumination source, particularly at least one laser source and / or at least one incandescent lamp and / or at least one semiconductor light source, such as at least one light-emitting diode, particularly organic and / or inorganic light-emitting diodes. For example, the light emitted by the illumination source may have a wavelength in the range of 300 to 500 nm. Additionally or alternatively, light in the infrared spectral range (such as in the range of 780 nm to 3.0 μm) may be used. Specifically, light in a portion of the near-infrared region may be used, where silicon photodiodes are specifically applicable in the range of 700 nm to 1000 nm. Considering their generally defined beam profiles and other operability characteristics, it is particularly preferred to use at least one laser source as the illumination source. Here, it may be preferred to use a single laser source, particularly in cases where it may be important to provide a compact scanning system that can be easily stored and transported by the user. Accordingly, the illumination source may preferably be an integral part of the detector and may thus be particularly integrated into the detector, such as integrated into the housing of the detector. In a preferred embodiment, in particular, the housing of the scanning system may include at least one display configured to provide distance-related information to the user in an easily readable manner, for example. In other preferred embodiments, in particular, the housing of the scanning system may additionally include at least one button that may be configured to operate at least one function related to the scanning system, such as for setting one or more operating modes. In other preferred embodiments, in particular, the housing of the scanning system may additionally include at least one fastening unit that may be configured to fasten the scanning system to another surface, such as rubber feet, a base plate, or a wall holder, such as a substrate or holder including magnetic material, particularly for improving the accuracy of distance measurement and / or the operability of the scanning system by the user.

[0172] In particular, the illumination source of the scanning system can thus emit a single laser beam, which can be configured to illuminate a single point located on the surface of the object. By using at least one detector according to the present invention, at least one item of information regarding the distance between at least one point and the scanning system can thus be generated. Thus, preferably, the distance between the illumination system included in the scanning system and the single point generated by the illumination source can be determined, such as by employing evaluation means included in at least one detector. However, the scanning system can further include an additional evaluation system, which can be particularly suitable for this purpose. Alternatively or additionally, the dimensions of the scanning system (in particular the housing of the scanning system) can be considered, and thus the distance between a specific point on the housing of the scanning system (such as the front edge or the rear edge of the housing) and the single point can be alternatively determined. The illumination source can be adapted to generate and / or project a point cloud. For example, the illumination source can include one or more of the following: at least one digital light processing projector, at least one LCoS projector, at least one spatial light modulator; at least one diffractive optical element; at least one light emitting diode array; at least one laser light source array.

[0173] Alternatively, the illumination source of the scanning system can emit two separate laser beams, which can be configured to provide a corresponding angle between the emission directions of the beams, such as a right angle, whereby two corresponding points located on the same object surface or on two different surfaces of two different objects can be illuminated. However, other values of the corresponding angle between the two separate laser beams are also feasible. This feature can be particularly used for indirect measurement functions, such as for deriving an indirect distance, such as may not be directly accessible due to the presence of one or more obstacles between the scanning system and the point or otherwise may be difficult to reach. For example, thus, it may be feasible to determine the value of the object height by measuring two separate distances and by using the Pythagorean formula to derive the height. In particular, in order to be able to maintain a predetermined level relative to the object, the scanning system can further include at least one level unit, in particular an integrated bubble vial, which can be used to maintain a user-defined level.

[0174] As a further alternative, the illumination source of the scanning system can emit a plurality of separate laser beams, such as a laser beam array, which can exhibit a corresponding pitch relative to each other, in particular a regular pitch, and can be arranged in such a way as to generate an array of points on at least one surface of at least one object. For this purpose, particularly suitable optical elements can be provided, such as beam splitting devices and mirrors, which can allow the generation of the said laser beam array. In particular, the illumination source can be guided to scan an area or volume by redirecting the beam in a periodic or non-periodic manner by using one or more movable mirrors.

[0175] Thus, the scanning system can provide a static arrangement of one or more points on one or more surfaces of one or more objects. Alternatively, the illumination source of the scanning system, particularly one or more laser beams, such as the above-described laser beam array, can be configured to provide one or more light beams that can exhibit varying intensities over time and / or may be subject to alternations in the emission direction over time, particularly by moving one or more mirrors, such as the micromirrors included in the micromirror array. As a result, the illumination source can be configured to scan a portion of at least one surface of at least one object as an image by using one or more light beams having alternating characteristics generated by at least one illumination source of the scanning system. In particular, the scanning system can thus use at least one line scan and / or line scan, such as scanning one or more surfaces of one or more objects sequentially or simultaneously. Thus, the scanning system can be adapted to measure angles by measuring three or more points, or the scanning system can be adapted to measure corners or narrow areas, such as the gable of a roof, which are difficult to access using a traditional measuring tape. As a non-limiting example, the scanning system can be used in a safety laser scanner (e.g., in a production environment), and / or for 3D scanning for determining object shape (such as in combination with 3D printing, body scanning, quality control), in building applications (e.g., as a rangefinder), in logistics applications (e.g., for determining the size or volume of a package), in home applications (e.g., in a robotic vacuum cleaner or lawn mower), or in other types of applications that may include a scanning step. As a non-limiting example, the scanning system can be used in industrial safety curtain applications. As a non-limiting example, the scanning system can be used to perform cleaning, vacuuming, mopping, or waxing functions, or yard or garden care functions, such as mowing or raking. As a non-limiting example, the scanning system can employ an LED illumination source with collimating optics and can be adapted to shift the frequency of the illumination source to a different frequency to obtain more accurate results and / or employ a filter to attenuate certain frequencies while transmitting other frequencies. As a non-limiting example, the scanning system and / or the illumination source can rotate as a whole or use a dedicated motor to rotate only specific optical components, such as mirrors, beam splitters, etc., such that in operation, the scanning system can have a full 360-degree view, or can even move and / or rotate out of plane to further increase the scanning area. Additionally, the illumination source can actively aim at a predetermined direction. Additionally, to allow rotation of a wired electrical system, a slip ring, optical data transmission, or inductive coupling can be employed.

[0176] As a non-limiting example, the scanning system can be attached to a tripod and pointed at an object or area having multiple corners and surfaces. One or more flexibly movable laser sources are attached to the scanning system. The one or more laser sources are moved so that they illuminate a point of interest. When a designated button on the scanning system is pressed, the position of the illuminated point relative to the scanning system is measured, and the position information is transmitted via a wireless interface to a mobile phone. The position information is stored in a mobile phone application. The laser sources are moved to illuminate other points of interest, the positions of which are measured and transmitted to the mobile phone application. The mobile phone application can transform the set of points into a 3D model by connecting adjacent points to a planar surface. The 3D model can be stored and further processed. The distances and / or angles between the measured points or surfaces can be directly displayed on a display attached to the scanning system or on the mobile phone to which the position information is transmitted.

[0177] As a non-limiting example, the scanning system can include two or more flexibly movable laser sources for projecting points and other movable laser sources for projecting lines. The lines can be used to arrange two or more laser spots along the line, and the display of the scanning system can display the distance between two or more laser spots that can be arranged along the line (such as equidistantly). In the case of two laser spots, a single laser source can be used, and one or more beam splitters or prisms are used to modify the distance of the projected points, where the beam splitter or prism can be moved so that the projected laser spots are separated or brought closer. Additionally, the scanning system can be adapted to project other patterns, such as right angles, circles, squares, triangles, etc., along which measurements can be made by projecting laser spots and measuring their positions.

[0178] As a non-limiting example, the scanning system can be applicable to a line scanning device. In particular, the scanning system can include at least one sensor line or row. Triangulation systems require a sufficient baseline such that it is not possible to detect in the near field. If the laser spot is tilted in the direction of the transfer device, near field detection can be possible. However, the tilt causes the spot to move out of the field of view, which limits the detection in the far field region. These near field and far field problems can be overcome by using a detector according to the present invention. In particular, the detector can include a CMOS line of optical sensors. The scanning system can be adapted to detect a plurality of light beams propagating from an object to the detector on the CMOS line. The light beams can be generated at different positions on the object or by the movement of the illumination source. As described in more detail above and below, the scanning system can be adapted to determine at least one ordinate of each of the light points.

[0179] As a non-limiting example, the scanning system can be adapted to support work using tools (such as wood or metalworking tools, such as saws, drills, etc.). Thus, the scanning system can be adapted to measure distances in two opposite directions and display the two measured distances or the sum of the distances in a display. Additionally, the scanning system can be adapted to measure the distance to the edge of a surface such that when the scanning system is placed on the surface, the laser spot automatically moves away from the scanning system along the surface until the distance measurement shows a sudden change due to a corner or edge of the surface. This enables the distance to the end of a wooden board to be measured when the scanning system is placed on the board but away from the end of the board. Further, the scanning system can measure the distance to the end of the wooden board in one direction and project a line or a circle or a point within a specified distance in the opposite direction. The scanning system can be adapted to project a line or a circle or a point within a certain distance depending on the distances measured in the opposite directions, such as depending on a predetermined total distance. This allows work to be done with a tool (such as a saw or a drill) at the projection position while placing the scanning system at a safe distance from the tool and simultaneously performing a process with the tool at a predetermined distance from the edge of the wooden board. Additionally, the scanning system can be adapted to project points or lines, etc. in two opposite directions within a predetermined distance. When the sum of the distances changes, only one of the projected distances changes.

[0180] As a non-limiting example, the scanning system can be adapted to be placed on a surface, such as a surface on which tasks such as cutting, sawing, drilling, etc. are performed, and project a line onto the surface at a predetermined distance, which can be adjusted, for example, by using a button on the scanning system.

[0181] As a non-limiting example, the scanning system can be used in a safety laser scanner (e.g., in a production environment), and / or in a 3D scanning device for determining the shape of an object (such as in combination with 3D printing, body scanning, quality control), in construction applications (e.g., as a rangefinder), in logistics applications (e.g., for determining the size or volume of a package), in home applications (e.g., in a robotic vacuum cleaner or a lawn mower), or in other types of applications that may include a scanning step.

[0182] The transmission device can be designed to preferably feed the light propagated from the object to the detector to the optical sensor successively. This feeding can optionally be achieved by means of imaging or additionally by means of the non-imaging characteristics of the transmission device. In particular, the transmission device can also be designed to collect the electromagnetic radiation before the electromagnetic radiation is subsequently fed to the optical sensor. The transmission device can also be wholly or partly an integral part of at least one optional irradiation source, for example by designing the irradiation source to provide a light beam with defined optical properties, such as at least one linear combination of a defined or precisely known beam profile, such as a Gaussian beam, in particular at least one laser beam with a known beam profile.

[0183] For potential embodiments of the optional illumination source, reference can be made to WO 2012 / 110924 A1. Nevertheless, other embodiments are also feasible. The light emitted from the object can originate from the object itself, but can alternatively have a different origin and propagate from that origin to the object and subsequently towards the lateral and / or longitudinal optical sensors. The latter case can be achieved, for example, by using at least one illumination source. The illumination source can be, for example, or include an ambient illumination source and / or can be or can include an artificial illumination source. By way of example, the detector itself can include at least one illumination source, such as at least one laser and / or at least one incandescent lamp and / or at least one semiconductor illumination source, such as at least one light-emitting diode, in particular an organic and / or inorganic light-emitting diode. Considering their generally defined beam profiles and other operability characteristics, it is particularly preferred to use one or more lasers as the illumination source or a part thereof. The illumination source itself can be a component of the detector or alternatively formed independently of the detector. The illumination source can in particular be integrated into the detector, for example integrated into the housing of the detector. Alternatively or additionally, at least one illumination source can also be integrated into at least one beacon device or integrated into one or more beacon devices and / or integrated into the object or connected to or spatially coupled to the object.

[0184] The light emitted from one or more optional beacon devices can, correspondingly, alternatively or additionally to the option that the light originates from the respective beacon device itself, be emitted from and / or excited by the illumination source. By way of example, the electromagnetic light emitted from the beacon device can be emitted by the beacon device itself and / or reflected by the beacon device and / or scattered by the beacon device before being fed to the detector. In this case, the emission and / or scattering of the electromagnetic radiation can be achieved without or with such an influence on the spectrum of the electromagnetic radiation. Thus, for example, a wavelength shift can also occur during scattering, for example according to Stokes or Raman. Furthermore, the emission of light can be excited, for example, by a main illumination source (such as an object or a partial region of an object that is excited to produce luminescence (in particular phosphorescence and / or fluorescence)). In principle, other emission processes are also possible. If reflection occurs, the object can have, for example, at least one reflective region, in particular at least one reflective surface. The reflective surface can be part of the object itself, but can also be, for example, a reflector that is connected or spatially coupled to the object, such as a reflector plate that is connected to the object. If at least one reflector is used, it can in turn be regarded as part of the detector, a part of the detector that is connected to the object, for example, independently of the other components of the detector.

[0185] The beacon device and / or at least one optional illumination source can generally emit light of at least one of the following: the ultraviolet spectral range, preferably in the range of 200 nm to 380 nm; the visible spectral range (380 nm to 780 nm); the infrared spectral range, preferably in the range of 780 nm to 3.0 micrometers, more preferably a part of the near-infrared region where silicon photodiodes are specifically applicable in the range of 700 nm to 1000 nm. For thermal imaging applications, the target can emit light in the far-infrared spectral range (preferably in the range of 3.0 micrometers to 20 micrometers). For example, at least one illumination source is adapted to emit light in the visible spectral range, preferably in the range of 500 nm to 780 nm, most preferably in the range of 650 nm to 750 nm, or 690 nm to 700 nm. For example, at least one illumination source is adapted to emit light in the infrared spectral range. However, other options are also feasible.

[0186] The feeding of the light beam to the optical sensor can be achieved in particular such that a light spot is generated on the optional sensor area of the optical sensor, for example, the light spot has a circular, elliptical, or cross-section of different configurations. For example, the detector can have a visible range, in particular a solid angle range and / or a spatial range, within which an object can be detected. Preferably, the transfer device can be designed such that, for example, when the object is arranged within the visible range of the detector, the light spot is completely arranged on the sensor area and / or the sensor region of the optical sensor. For example, a sensor area of corresponding size can be selected to ensure this condition.

[0187] In other aspects, the present invention discloses a method for determining at least one material property of at least one object by using at least one detector according to the present invention. The method includes the following steps:

[0188] a) Determining at least one reflected image of the object by using at least one sensor element of a matrix having an optical sensor, each optical sensor having a photosensitive area;

[0189] b) Determining the material property by evaluating at least one beam profile of the reflected image by using at least one evaluation device, wherein the evaluation includes:

[0190] b1) Determining at least one distance feature by applying at least one distance-dependent image filter Ф1 to the reflected image wherein the distance-dependent image filter is at least one filter selected from the group including: a photon depth ratio filter; a defocus depth filter; or a linear combination thereof; or is another distance-dependent image filter Ф 1其他 , which is through |ρ Ф1其他,Фz|≥0.40 is related to a photon depth ratio filter and / or a defocus depth filter or a linear combination thereof, where Ф z is one of a photon depth ratio filter and / or a defocus depth filter or a linear combination thereof,

[0191] b2) determining at least one material feature by applying at least one material-related image filter Ф2 to the reflected image where the material-related image filter is at least one filter selected from the group consisting of: a luminance filter; a spot shape filter; a squared norm gradient; a standard deviation; a smoothing filter such as a Gaussian filter or a median filter; a contrast filter based on gray level occurrence; an energy filter based on gray level occurrence; a homogeneity filter based on gray level occurrence; a dissimilarity filter based on gray level occurrence; a Lowe's energy filter; a threshold region filter; or a linear combination thereof; or is another material-related image filter Ф 2其他 , which is related to a luminance filter; a spot shape filter; a squared norm gradient; a standard deviation; a smoothing filter; an energy filter based on gray level occurrence; a homogeneity filter based on gray level occurrence; a dissimilarity filter based on gray level occurrence; a Lowe's energy filter; or a threshold region filter; or a linear combination thereof by |ρ Ф2其他,Фm |≥0.40, where Ф m is one of a luminance filter; a spot shape filter; a squared norm gradient; a standard deviation; a smoothing filter; an energy filter based on gray level occurrence; a homogeneity filter based on gray level occurrence; a dissimilarity filter based on gray level occurrence; a Lowe's energy filter; or a threshold region filter; or a linear combination thereof,

[0192] b3) determining the ordinate z and the material property m by evaluating the distance feature and the material feature The method steps can be executed in a given order or can be executed in a different order. In addition, there may be one or more additional method steps not listed. In addition, one, more than one, or even all of the method steps can be repeated. For details, options, and definitions, reference can be made to the detector discussed above. Thus, specifically, as described above, the method can include using a detector according to the present invention (such as according to one or more embodiments given above or further detailed below).

[0193] In other aspects, the present invention discloses a method for determining at least one material property of at least one object by using at least one detector according to the present invention. The method includes the following steps:

[0194]

[0195] ​a) determining at least one reflected image of an object by using at least one sensor element of a matrix with optical sensors, each optical sensor having a photosensitive area;

[0196] b) determining a material property by evaluating at least one beam profile of the reflected image by using at least one evaluation device, wherein the evaluation includes:

[0197] b1) determining at least one distance feature by applying at least one distance - related image filter Ф1 to the reflected image wherein the distance - related image filter is at least one filter selected from the group comprising: a photon depth ratio filter; a defocus depth filter; or a linear combination thereof; or is another distance - related image filter Ф 1其他 , which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, where Ф z is one of the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof,

[0198] b2) determining at least one material feature by applying at least one material - related image filter Ф2 to the reflected image wherein the material - related image filter is at least one filter by hypothesis testing, wherein the hypothesis testing uses the null hypothesis that the filter does not distinguish material classifiers and the alternative hypothesis that the filter distinguishes at least two material classifiers, and wherein the filter passes the hypothesis testing if the p - value p is less than or equal to a predefined significance level,

[0199] b3) determining the ordinate z and the material property m by evaluating the distance feature and the material feature .

[0200] The method steps can be performed in the given order or in a different order. Additionally, there may be one or more additional method steps not listed. Further, one, more than one, or even all of the method steps can be repeated. For details, options, and definitions, reference can be made to the detector discussed above. Thus, specifically, as outlined above, the method can include using a detector according to the present invention (such as according to one or more of the embodiments given above or further detailed below).

[0201] In other aspects, the present invention discloses a method for determining at least one material property of at least one object by using at least one detector according to the present invention. The method includes the following steps:

[0202] a) determining at least one reflected image of an object by using at least one sensor element of a matrix with optical sensors, each optical sensor having a photosensitive area;

[0203] b) determining a material property by evaluating at least one beam profile of the reflected image by using at least one evaluation device, wherein the evaluation includes:

[0204] b1) determining at least one distance feature by applying at least one distance - related image filter Ф1 to the reflected image wherein the distance - related image filter is at least one filter selected from the group including: photon depth ratio filter; defocus depth filter; or a linear combination thereof; or is another distance - related image filter Ф 1其他 , which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, where Ф z is one of the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof,

[0205] b2) determining at least one material feature by applying at least one material - related image filter Ф2 to the reflected image

[0206] b3) determining the ordinate z and the material property m by evaluating the distance feature and the material feature .

[0207] The method steps can be executed in a given order or can be executed in a different order. In addition, there may be one or more additional method steps not listed. In addition, one, more than one, or even all of the method steps can be repeated. For details, options, and definitions, reference can be made to the detector discussed above. Thus, specifically, as outlined above, the method can include using a detector according to the present invention (such as according to one or more of the embodiments given above or further detailed below).

[0208] In other aspects of the present invention, for use purposes, there is provided the use of a detector according to the present invention (such as according to one or more of the embodiments given above or further detailed below), the use being selected from the group including: position measurement in traffic technology; entertainment applications; security applications; surveillance applications; safety applications; human - machine interface applications; tracking applications; photographic applications; imaging applications or camera applications; mapping applications for generating a map of at least one space; homing or tracking beacon detectors for vehicles; outdoor applications; mobile applications; communication applications; machine vision applications; robot applications; quality control applications; manufacturing applications.

[0209] The object can typically be a living or inanimate object. The detector or detector system can even include at least one object, which thereby forms part of the detector system. However, preferably, the object can move independently of the detector in at least one spatial dimension. The object can typically be any object. In one embodiment, the object can be a rigid object. Other embodiments are possible, such as embodiments where the object is a non-rigid object or an object that can change its shape.

[0210] For further uses of the detector and device of the present invention, reference is made to WO 2018 / 091649 A1, WO 2018 / 091638 A1 and WO 2018 / 091640 A1, the contents of which are incorporated herein by reference.

[0211] Specifically, the present application can be applied to the field of photography. Thus, the detector can be part of a photographic device, specifically part of a digital camera. Specifically, the detector can be used for 3D photography, specifically for digital 3D photography. Thus, the detector can form a digital 3D camera or can be part of a digital 3D camera. As used herein, the term photography generally refers to the technique of acquiring image information of at least one object. As further used herein, a camera is generally a device configured to perform photography. As further used herein, the term digital photography generally refers to the technique of acquiring image information of at least one object by using a plurality of photosensitive elements configured to generate electrical signals indicative of the irradiation intensity and / or color, preferably digital electrical signals. As further used herein, the term 3D photography generally refers to the technique of acquiring image information of at least one object in three spatial dimensions. Thus, a 3D camera is a device configured to perform 3D photography. A camera can typically be configured to acquire a single image, such as a single 3D image, or can be configured to acquire a plurality of images, such as an image sequence. Thus, the camera can also be a video camera configured for video applications, such as for acquiring a digital video sequence.

[0212] Thus, generally, the present invention further relates to a camera for imaging at least one object, specifically a digital camera, more specifically a 3D camera or a digital 3D camera. As outlined above, the term imaging as used herein generally refers to acquiring image information of at least one object. The camera includes at least one detector according to the present invention. As outlined above, the camera can be configured to acquire a single image or for acquiring a plurality of images (such as an image sequence), preferably for acquiring a digital video sequence. Thus, by way of example, the camera can be or can include a video camera. In the latter case, the camera preferably includes a data memory for storing the image sequence.

[0213] As used in the present invention, the term "position" generally refers to at least one item of information among one or more of the absolute position and orientation of an object with respect to one or more points. Thus, specifically, the position can be determined in the coordinate system of the detector (such as in a Cartesian coordinate system). However, additionally or alternatively, other types of coordinate systems can be used, such as polar coordinate systems and / or spherical coordinate systems.

[0214] As described above and as will be further described in detail below, the present invention can preferably be applied to the fields of human - machine interfaces, sports, and / or computer games. Thus, preferably, the object can be selected from the group including the following items: sports equipment items, preferably items selected from the group consisting of rackets, clubs, and bats; clothing, hat, and shoe items. Other embodiments are feasible.

[0215] Regarding the coordinate system for determining the position of the object (which can be the coordinate system of the detector), the detector can constitute the coordinate system, where the optical axis of the detector forms the z - axis, and where, additionally, an x - axis and a y - axis perpendicular to the z - axis and perpendicular to each other can be provided. As an example, the detector and / or a part of the detector can be located at a specific point in this coordinate system, such as at the origin of this coordinate system. In this coordinate system, the direction parallel or anti - parallel to the z - axis can be considered the longitudinal direction, and the coordinate along the z - axis can be considered the ordinate. Any direction perpendicular to the longitudinal direction can be considered the transverse direction, and the x - coordinate and / or the y - coordinate can be considered the abscissa.

[0216] Alternatively, other types of coordinate systems can be used. Thus, as an example, a polar coordinate system can be used, where the optical axis forms the z - axis and the distance from the z - axis and the polar angle can be used as additional coordinates. Similarly, the direction parallel or anti - parallel to the z - axis can be considered the longitudinal direction, and the coordinate along the z - axis can be considered the ordinate. Any direction perpendicular to the z - axis can be considered the transverse direction, and the polar coordinates and / or the polar angle can be considered the abscissa.

[0217] The detector can be a device configured to provide at least one item of information regarding the position of at least one object and / or a part thereof. Thus, the position can refer to an information item that completely describes the position of the object or its part (preferably) in the coordinate system of the detector, or can refer to partial information that only partially describes the position. The detector can generally be a device configured to detect a light beam (such as a light beam propagating from a beacon device towards the detector).

[0218] The evaluation device and the detector can be fully or partially integrated into a single device. Thus, generally, the evaluation device can also form part of the detector. Alternatively, the evaluation device and the detector can be fully or partially embodied as separate devices. The detector can include other components.

[0219] The detector can be a stationary device or a mobile device. Additionally, the detector can be a stand-alone device or can form part of another device such as a computer, a vehicle, or any other device. Additionally, the detector can be a hand-held device. Other embodiments of the detector are feasible.

[0220] The evaluation device can be or can include one or more integrated circuits such as one or more application-specific integrated circuits (ASICs), and / or one or more data processing devices such as one or more computers, preferably one or more microcomputers and / or microcontrollers, field-programmable arrays, or digital signal processors. Additional components can be included such as one or more preprocessing devices and / or data acquisition devices such as one or more devices for receiving and / or preprocessing sensor signals such as one or more AD converters and / or one or more filters. Additionally, the evaluation device can include one or more measuring devices such as one or more measuring devices for measuring current and / or voltage. Additionally, the evaluation device can include one or more data storage devices. Additionally, the evaluation device can include one or more interfaces such as one or more wireless interfaces and / or one or more wired binding interfaces.

[0221] At least one evaluation device can be configured to execute at least one computer program such as at least one computer program configured to execute or support one or more or even all of the method steps according to the method of the present invention. As an example, one or more algorithms can be implemented that can determine the position of an object by using sensor signals as input variables.

[0222] The evaluation device can be connected to or can include at least one other data processing device that can be used for one or more of the display, visualization, analysis, distribution, communication, or further processing of information such as information obtained by an optical sensor and / or by the evaluation device. As an example, the data processing device can be connected to or incorporated with at least one of a display, a projector, a monitor, an LCD, a TFT, a speaker, a multi-channel sound system, an LED pattern, or other visualization devices. It can further be connected to or incorporated with a communication device or communication interface, connector, or port capable of sending encrypted or unencrypted information using one or more of email, text message, phone, Bluetooth, Wi-Fi, infrared, or an Internet interface, port, or connection. It can further be connected to or combined with a processor, a graphics processor, a CPU, an Open Multimedia Application Platform (OMAP TM) at least one of an integrated circuit, a system-on-chip (such as products from the Apple A series or the Samsung S3C2 series), a microcontroller or a microprocessor, one or more memory blocks (such as ROM, RAM, EEPROM or flash memory), a timing source (such as an oscillator or a phase-locked loop), a counter timer, a real-time timer or a power-on reset generator, a voltage regulator, a power management circuit, or a DMA controller. Each unit can be further connected via a bus such as the AMBA bus, or integrated in an Internet of Things or Industry 4.0 type network.

[0223] The evaluation device and / or the data processing device can be connected via other external interfaces or ports, or have other external interfaces or ports, such as one or more of the following: a serial or parallel interface or port, USB, Centronics port, FireWire, HDMI, Ethernet, Bluetooth, RFID, Wi-Fi, USART or SPI, or an analog interface or port (such as one or more of ADC or DAC), or a standardized interface or port for connecting to other devices, such as a 2D camera device using an RGB interface such as CameraLink. The evaluation device and / or the data processing device can be further connected via one or more of an inter-processor interface or port, an FPGA-FPGA interface, or a serial or parallel interface port. The evaluation device and the data processing device can be further connected to one or more of an optical disc drive, a CD-RW drive, a DVD+RW drive, a flash drive, a memory card, a disk drive, a hard disk drive, a solid state disk or a solid state drive.

[0224] The evaluation device and / or the data processing device can be connected via one or more other external connectors, or have one or more other external connectors, such as one or more of a phone connector, an RCA connector, a VGA connector, an androgynous connector, a USB connector, an HDMI connector, an 8P8C connector, a BCN connector, an IEC60320 C14 connector, an optical fiber connector, a D-subminiature connector, an RF connector, a coaxial connector, a SCART connector, an XLR connector, and / or can include at least one suitable socket for one or more of these connectors.

[0225] Possible embodiments of a single device that includes one or more detectors, evaluation devices, or data processing devices according to the present invention (e.g., including one or more of an optical sensor, an optical system, an evaluation device, a communication device, a data processing device, an interface, a system-on-chip, a display device, or other electronic devices) are: a mobile phone, a personal computer, a tablet PC, a television, a gaming console, or other entertainment devices. In a further embodiment, a 3D camera function, which will be described in more detail below, can be integrated into a device that can be used for a conventional 2D digital camera, with no significant difference in the housing or appearance of the device, where the significant difference for the user may only be the function of obtaining and / or processing 3D information. Additionally, the device according to the present invention can be used for a 360° digital camera or a surround camera.

[0226] Specifically, embodiments that include a detector and / or a part thereof (e.g., an evaluation device and / or a data processing device) can be: a mobile phone, which includes a display device, a data processing device, an optical sensor, optional sensor optics, and an evaluation device for the function of a 3D camera. The detector according to the present invention can specifically be adapted for integration in an entertainment device and / or a communication device such as a mobile phone.

[0227] The human-machine interface can include a plurality of beacon devices that are configured to be directly or indirectly attached to the user and at least one of being held by the user. Thus, the beacon devices can be independently attached to the user respectively by any suitable means (e.g., by a suitable fixing device). Additionally or alternatively, the user can hold and / or carry at least one beacon device or one or more of the beacon devices in his or her hand and / or by wearing at least one beacon device and / or clothes including beacon devices on a body part.

[0228] A beacon device can generally be any device that can be detected by at least one detector and / or facilitates being detected by at least one detector. Thus, as described above or will be described in more detail below, the beacon device can be an active beacon device configured to generate at least one light beam to be detected by the detector, e.g., by having one or more illumination sources for generating at least one light beam. Additionally or alternatively, the beacon device can be designed as a passive beacon device completely or partially, e.g., by providing one or more reflective elements configured to reflect light beams generated by a separate illumination source. The at least one beacon device can be permanently or temporarily attached to the user in a direct or indirect manner and / or can be carried or held by the user. The attachment can be achieved by using one or more attachment devices and / or by the user himself or herself, e.g., by the user holding the at least one beacon device by hand and / or by the user wearing the beacon device.

[0229] Additionally or alternatively, the beacon device can be at least one of attached to an object and integrated into an object held by a user, which, for the purposes of the present invention, should be included in the meaning of the option of a user holding the beacon device. Thus, as described in more detail below, the beacon device can be attached to or integrated into a control element, which can be part of a human-machine interface and can be held or carried by a user, the orientation of which can be recognized by the detector device. Thus, in general, the present invention also relates to a detector system, which includes at least one detector device according to the present invention and can further include at least one object, wherein the beacon device is one of attached to the object, held by the object, and integrated into the object. As an example, the object can preferably form a control element, the orientation of which can be recognized by the user. Thus, as described above or further described below, the detector system can be part of a human-machine interface. As an example, the user can operate the control element in a specific manner to send one or more items of information to a machine, for example to send one or more commands to a machine.

[0230] Alternatively, the detector system can be used in other ways. Thus, as an example, the object of the detector system can be different from the user or a body part of the user and, as an example, can be an object that moves independently of the user. As an example, the detector system can be used to control a device and / or an industrial process, such as a manufacturing process and / or a robotic process. Thus, as an example, the object can be a machine and / or a machine part, such as a robotic arm, the orientation of which can be detected by using the detector system.

[0231] The human-machine interface can be configured such that the detector device generates at least one item of information about the position of the user or at least one body part of the user. Specifically, in the case where the manner of attachment of at least one beacon device to the user is known, at least one item of information about the position and / or orientation of the user or a body part of the user can be obtained by evaluating the position of at least one beacon device.

[0232] The beacon device is preferably one of a beacon device attachable to the body or a body part of the user and a beacon device holdable by the user. As described above, the beacon device can be designed, in whole or in part, as an active beacon device. Thus, the beacon device can include at least one irradiation source configured to generate at least one light beam to be transmitted to the detector, which light beam is preferably at least one light beam having known beam characteristics. Additionally or alternatively, the beacon device can include at least one emitter configured to reflect the light generated by the irradiation source, thereby generating a reflected light beam to be transmitted to the detector.

[0233] Objects that can form part of a detector system can generally have any shape. Preferably, as described above, an object that is part of a detector system can be a control element that can be operated by a user (e.g., manually). As an example, the control element can be or can include at least one element selected from the group consisting of: gloves, coats, hats, shoes, pants and suits, a cane that can be held by hand, a bat, a club, a racket, a crutch, a toy (e.g., a toy gun). Thus, as an example, the detector system can be part of a human-machine interface and / or an entertainment device.

[0234] As used herein, an entertainment device is a device that can be used for the leisure and / or entertainment purposes of one or more users (hereinafter also referred to as one or more players). As an example, the entertainment device can be used for the purpose of playing games, preferably for the purpose of computer games. Thus, the entertainment device can be implemented as a computer, a computer network, or a computer system, or can include a computer, a computer network, or a computer system that runs one or more game software programs.

[0235] The entertainment device includes at least one human-machine interface according to the present invention (e.g., according to one or more of the embodiments disclosed above and / or according to one or more of the embodiments disclosed below). The entertainment device is designed to allow at least one item of information to be input by a player by means of the human-machine interface. The at least one item of information can be transmitted to the controller and / or computer of the entertainment device, and / or can be used by the controller and / or computer of the entertainment device. The at least one item of information preferably can include at least one command configured to affect the game process. Thus, as an example, the at least one item of information can include at least one item of information related to at least one orientation of the player and / or one or more body parts of the player, thereby allowing the player to simulate specific positions and / or orientations and / or actions required for the game. As an example, one or more of the following movements can be simulated and communicated to the controller and / or computer of the entertainment device: dancing; running; jumping; waving a racket; waving a bat; waving a club; pointing an object at another object, e.g., pointing a toy gun at a target.

[0236] The entertainment device, as part or as a whole, preferably the controller and / or computer of the entertainment device, is designed to change the entertainment function according to the information. Thus, as described above, the game process can be affected according to at least one item of information. Thus, the entertainment device can include one or more controllers, which can be separate from the evaluation device of at least one detector and / or can be completely or partially identical to at least one evaluation device, or can even include at least one evaluation device. Preferably, at least one controller can include one or more data processing devices, such as one or more computers and / or microcontrollers.

[0237] As further used herein, a tracking system is a device configured to collect information related to at least one object and / or a series of past positions of at least a portion of the object. Additionally, the tracking system can be configured to provide information related to at least one predicted future position and / or orientation of at least one object or at least one portion of the object. The tracking system can have at least one tracking controller, which can be embodied, in whole or in part, as an electronic device, preferably as at least one data processing device, more preferably as at least one computer or microcontroller. Further, the at least one tracking controller can comprise, in whole or in part, at least one evaluation device and / or can be part of at least one evaluation device and / or can be identical, in whole or in part, to at least one evaluation device.

[0238] The tracking system includes at least one detector according to the present invention, such as at least one detector disclosed in one or more of the embodiments listed above and / or in one or more of the embodiments below. The tracking system further includes at least one tracking controller. The tracking controller is configured to track a series of positions of an object at a particular point in time, such as by recording multiple sets of data or data pairs, each set of data or data pair including at least one position information and at least one time information.

[0239] The tracking system can further include at least one detector system according to the present invention. Thus, in addition to at least one detector and at least one evaluation device and optionally at least one beacon device, the tracking system can further include the object itself or a portion of the object, such as at least one control element, which includes a beacon device or at least one beacon device, wherein the control element is directly or indirectly attached to or integrated into the object to be tracked.

[0240] The tracking system can be configured to initiate one or more actions of the tracking system itself and / or one or more individual devices. For this latter purpose, the tracking system, preferably the tracking controller, can have one or more wireless and / or wired interfaces and / or other types of control connections for initiating at least one action. Preferably, at least one tracking controller can be configured to initiate at least one action based on at least one actual position of the object. As an example, the action can be selected from the group consisting of: prediction of a future position of the object; pointing at least one device at the object; pointing at least one device at the detector; illuminating the object; illuminating the detector.

[0241] As an example of the application of the tracking system, the tracking system can be used to continuously point at least one first object at least one second object, even if the first object and / or the second object may move. Additionally, potential examples can be found in industrial applications (such as in robotics), and / or for continuously working on an article even if the article is moving, such as during manufacturing in a manufacturing line or an assembly line. Additionally or alternatively, the tracking system can be used for irradiation purposes, such as for continuously irradiating an object by continuously pointing an irradiation source at the object, even if the object may be moving. Other applications can be found in communication systems, such as for continuously sending information to a moving object by pointing a transmitter at the moving object.

[0242] In other aspects of the present invention, an inertial measurement unit for an electronic device is disclosed. The inertial measurement unit is adapted to receive data determined by at least one detector according to the present invention. The inertial measurement unit is further adapted to receive data determined by at least one other sensor selected from the group consisting of: a wheel speed sensor, a steering rate sensor, an inclination sensor, an orientation sensor, a motion sensor, a magnetohydrodynamic sensor, a force sensor, an angle sensor, an angular rate sensor, a magnetic field sensor, a magnetometer, an accelerometer; a gyroscope. The inertial measurement unit is adapted to determine at least one characteristic of the electronic device by evaluating data from the detector and the at least one other sensor, the characteristic being selected from the group consisting of: spatial position, relative or absolute motion in space, rotation, acceleration, orientation, angular position, inclination, steering rate, speed.

[0243] The inertial measurement unit may include a detector according to the present invention and / or may be connected to the detector via at least one data connection. The evaluation means and / or at least one processing means of the inertial measurement unit may be configured to determine at least one combined distance information, in particular using at least one recursive filter. The recursive filter may be configured to determine the combined distance information taking into account other sensor data and / or other parameters (such as other sensor data from other sensors of the inertial measurement unit). For the definition and embodiments of the inertial measurement unit, reference is made to the description of the detector.

[0244] Generally speaking, in the context of the present invention, the following embodiments are considered to be preferred:

[0245] Embodiment 1: A detector for identifying at least one material property m, comprising:

[0246] - at least one sensor element, which includes a matrix of optical sensors, each optical sensor having a photosensitive area, wherein the sensor element is configured to record at least one reflected image of a light beam originating from at least one object;

[0247] - At least one evaluation device configured to determine a material property by evaluating at least one beam profile of a reflected image,

[0248] wherein the evaluation device is configured to determine at least one distance feature by applying at least one distance - related image filter Ф1 to the reflected image wherein the distance - related image filter is at least one filter selected from the group comprising: a photon depth ratio filter; a defocus depth filter; or a linear combination thereof; or is another distance - related image filter Ф 1其他 , which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, where Ф z is one of the photon depth ratio filter or the defocus depth filter or a linear combination thereof,

[0249] wherein the evaluation device is configured to determine at least one material feature by applying at least one material - related image filter Ф2 to the reflected image wherein the material - related image filter is at least one filter selected from the group comprising: a brightness filter; a spot - shape filter; a squared - norm gradient; a standard deviation; a smoothing filter such as a Gaussian filter or a median filter; a contrast filter based on gray - level occurrence; an energy filter based on gray - level occurrence; a homogeneity filter based on gray - level occurrence; a dissimilarity filter based on gray - level occurrence; a Lowe's energy filter; a threshold region filter; or a linear combination thereof; or is another material - related image filter Ф 2其他 , which is related to one or more of the brightness filter, the spot - shape filter, the squared - norm gradient, the standard deviation, the smoothing filter, the energy filter based on gray - level occurrence, the homogeneity filter based on gray - level occurrence, the dissimilarity filter based on gray - level occurrence, the Lowe's energy filter, or the threshold region filter, or a linear combination thereof by |ρ Ф2其他,Фm |≥0.40, where Ф m is one of the brightness filter, the spot - shape filter, the squared - norm gradient, the standard deviation, the smoothing filter, the energy filter based on gray - level occurrence, the homogeneity filter based on gray - level occurrence, the dissimilarity filter based on gray - level occurrence, the Lowe's energy filter, or the threshold region filter, or a linear combination thereof,

[0250] wherein the evaluation device is configured to determine the ordinate z and the material property m by evaluating the distance feature and the material feature .

[0251] Example 2: The detector according to the previous example, wherein the material-related image filter is at least one filter by means of a hypothesis test, wherein the hypothesis test uses the null hypothesis that the filter does not distinguish material classifiers and the alternative hypothesis that the filter distinguishes at least two material classifiers, and wherein the filter passes the hypothesis test if the p-value p is less than or equal to a predefined significance level.

[0252] Example 3: The detector according to the previous example, wherein p ≤ 0.075, preferably p ≤ 0.05, more preferably p ≤ 0.025, and most preferably p ≤ 0.01.

[0253] Example 4: The detector according to any one of the previous examples, wherein at least one material property is a property selected from the group comprising: scattering coefficient, translucency, transparency, deviation from Lambertian surface reflection, speckle, and the like.

[0254] Example 5: The detector according to any one of the previous examples, wherein another distance-related image filter Ф 1其他 is related to one or more of the distance-related image filters Ф Ф1其他,Фz by |ρ z | ≥ 0.60, preferably |ρ Ф1其他,Фz | ≥ 0.80.

[0255] Example 6: The detector according to any one of the previous examples, wherein another material-related image filter Ф 2其他 is related to one or more of the material-related image filters Ф Ф2其他,Фm by |ρ m | ≥ 0.60, preferably |ρ Ф2其他,Фm | ≥ 0.80.

[0256] Example 7: The detector according to any one of the previous examples, wherein the material property m and / or the ordinate z are determined by using a predefined relationship between z and m.

[0257] Example 8: The detector according to any one of the previous examples, wherein the material property m and / or the ordinate z are determined by the function z and / or determined.

[0258] Example 9: The detector according to any one of the previous examples, wherein the evaluation device is configured to apply the distance-related image filter and the material-related image filter to the reflected image simultaneously.

[0259] Example 10: A detector according to any one of the preceding examples, wherein the evaluation means is configured to determine whether at least one of Ф1 or Ф2 is a characteristic of another image filter or a function of, or whether at least one of Ф1 or Ф2 is a function of at least one other image filter, wherein the evaluation means is configured to apply a distance-dependent image filter and a material-dependent image filter to the reflected image sequentially or recursively.

[0260] Example 11: A detector according to the previous example, wherein the evaluation means is configured to determine at least one of z and / or m by applying at least one other filter that depends on and at least one of to the reflected image.

[0261] Example 12: A detector according to any one of the preceding examples, wherein the photon depth ratio filter comprises: evaluating a combined signal Q of at least two sensor signals from sensor elements, wherein the evaluation means is configured to derive the combined signal Q by dividing sensor signals, dividing multiples of sensor signals, dividing linear combinations of sensor signals, or a combination of one or more of these, and wherein the evaluation means is configured to use at least one predetermined relationship between the combined signal Q and a distance characteristic to determine the distance characteristic

[0262] Example 13: A detector according to the previous example, wherein the evaluation means is configured to derive the combined signal Q by

[0263]

[0264] where x and y are abscissas, A1 and A2 are different regions of the beam profile, and E(x, y, z o ) represents the beam profile at a given object distance z o where each of the sensor signals includes at least one piece of information about at least one region of the beam profile of the light beam propagating from the object to the detector.

[0265] Example 14: A detector according to the previous example, wherein the photosensitive regions are arranged such that the first sensor signal includes information about a first region of the beam profile and the second sensor signal includes information about a second region of the beam profile, wherein the first region of the beam profile and the second region of the beam profile are either adjacent or overlapping regions, or both.

[0266] Example 15: The detector according to the previous example, wherein the evaluation device is configured to determine a first region of the beam profile and a second region of the beam profile, wherein the first region of the beam profile includes substantially edge information of the beam profile, and the second region of the beam profile includes substantially central information of the beam profile, wherein the edge information includes information related to the number of photons in the first region of the beam profile, and the central information includes information related to the number of photons in the second region of the beam profile.

[0267] Example 16: The detector according to any one of the previous examples, wherein the defocus depth filter includes: using at least one convolution-based algorithm, such as a defocus depth algorithm, wherein the evaluation device is configured to determine the distance feature by optimizing at least one blurring function f a to determine the distance feature wherein the blurring function is optimized by changing the parameters of at least one blurring function.

[0268] Example 17: The detector according to the previous example, wherein the reflected image is a blurred image i b wherein the evaluation device is configured to reconstruct the distance feature from the blurred image i b and the blurring function f a to reconstruct the distance feature wherein the blurring function f is minimized by changing the parameter σ of the blurring function a and at least one other image i' b of the convolution between and the blurred image i b the difference between, min ‖(i′ b *f a (σ(z)) - i b )‖, to determine the distance feature

[0269] Example 18: The detector according to any one of the previous two examples, wherein at least one blurring function f a is a function or composite function composed of at least one function from the group including the following: Gaussian function, sine function, parabolic cylinder function, square function, Lorentz function, radial function, polynomial, Hermite polynomial, Zernike polynomial, Legendre polynomial.

[0270] Example 19: The detector according to any one of the previous examples, wherein the sensor element includes at least one CMOS sensor.

[0271] Example 20: A detector according to any of the preceding examples, wherein the detector comprises at least one irradiation source, wherein the irradiation source is configured to generate at least one irradiation pattern for irradiating an object, wherein the irradiation pattern comprises at least one pattern selected from the group consisting of: at least one dot pattern, in particular a pseudo-random dot pattern; a random dot pattern or a quasi-random pattern; at least one Sobol pattern; at least one quasi-periodic pattern; at least one pattern comprising at least one known feature; at least one regular pattern; at least one triangular pattern; at least one hexagonal pattern; at least one rectangular pattern; at least one pattern comprising a protruding uniform tiling; at least one line pattern comprising at least one line; at least one line pattern comprising at least two lines such as parallel lines or intersecting lines.

[0272] Example 21: A detector according to the previous example, wherein the irradiation source comprises at least one laser source and at least one diffractive optical element.

[0273] Example 22: A detector according to any of the two preceding examples, wherein the detector comprises at least two sensor elements, each sensor element having a matrix of optical sensors, wherein at least one first sensor element and at least one second sensor element are located at different spatial positions, wherein the evaluation means is configured to select at least one image determined by the first sensor element or the second sensor element as a reflected image, and is configured to select at least one image determined by the other sensor element of the first sensor element or the second sensor element as a reference image.

[0274] Example 23: A detector according to the previous example, wherein the evaluation means is configured to select at least one reflection feature of the reflected image, wherein the evaluation means is configured to determine at least one distance estimate of the selected reflection feature in the reflected image given by a distance feature and an error interval ±ε, wherein the evaluation means is configured to determine at least one displacement region in the reference image corresponding to the distance estimate, wherein the evaluation means is configured to match the selected reflection feature with at least one reference feature within the displacement region, wherein the evaluation means is configured to determine the displacement between the matched reference feature and the selected reflection feature, wherein the evaluation means is configured to use a predetermined relationship between the ordinate and the displacement to determine the ordinate of the matched feature.

[0275] Example 24: A detector according to the previous example, wherein the reference image and the reflected image are images of an object determined at different spatial positions with a fixed distance, wherein the evaluation means is adapted to determine the epipolar line in the reference image, wherein the displacement region extends along the epipolar line, wherein the evaluation means is adapted to determine the distance feature The corresponding reference features along the epipolar line and determine the range of the displacement region along the epipolar line corresponding to the error interval ±ε.

[0276] Example 25: A detector for identifying at least one material property m, comprising:

[0277] - At least one sensor element, which includes a matrix of optical sensors, each optical sensor having a photosensitive region, wherein the sensor element is configured to record at least one reflected image of a light beam originating from at least one object;

[0278] - At least one evaluation device, which is configured to determine the material property by evaluating at least one beam profile of the reflected image,

[0279] wherein the evaluation device is configured to determine at least one distance feature by applying at least one distance-related image filter Ф1 to the reflected image wherein the distance-related image filter is at least one filter selected from the group consisting of: a photon depth ratio filter; a defocus depth filter; or a linear combination thereof; or is another distance-related image filter Ф 1其他 , which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, wherein Ф z is one of the photon depth ratio filter or the defocus depth filter or a linear combination thereof,

[0280] wherein the evaluation device is configured to determine at least one material feature by applying at least one material-related image filter Ф2 to the reflected image wherein the material-related image filter is at least one filter by hypothesis testing, wherein the hypothesis testing uses the null hypothesis that the filter does not distinguish material classifiers and the alternative hypothesis that the filter distinguishes at least two material classifiers, wherein if the p-value p is less than or equal to a predefined significance level, the filter passes the hypothesis testing,

[0281] wherein the evaluation device is configured to determine the ordinate z and the material property m by evaluating the distance feature and the material feature .

[0282] Example 26: The detector according to the previous example, wherein the hypothesis testing is based on a data set of beam profile images, wherein each beam profile image corresponds to a material classifier and a distance.

[0283] Example 27: The detector according to any one of Examples 25 and 26, wherein p≤0.075, preferably, p≤0.05, more preferably, p≤0.025, most preferably, p≤0.01.

[0284] Example 28: The detector according to any one of Examples 25 to 27, wherein at least one material property is a property selected from the group consisting of: scattering coefficient, translucency, transparency, deviation from Lambertian surface reflection, speckle, etc.

[0285] Example 29: The detector according to any one of Examples 25 to 28, wherein another distance-related image filter Ф 1其他 is related to one or more of the distance-related image filters Ф Ф1其他,Фz by |ρ z |≥0.60, preferably, |ρ Ф1其他,Фz |≥0.80.

[0286] Example 30: The detector according to any one of Examples 25 to 29, wherein another material-related image filter Ф 2其他 is related to one or more of the material-related image filters Ф Ф2其他,Фm by |ρ m |≥0.60, preferably, |ρ Ф2其他,Фm |≥0.80.

[0287] Example 31: The detector according to any one of Examples 25 to 30, wherein the material-related image filter is at least one filter selected from the group consisting of: luminance filter; spot shape filter; squared norm gradient; standard deviation; smoothing filter, such as Gaussian filter or median filter; contrast filter based on gray level occurrence; energy filter based on gray level occurrence; homogeneity filter based on gray level occurrence; dissimilarity filter based on gray level occurrence; Lowe's energy filter; threshold region filter; or a linear combination thereof; or is another material-related image filter Ф 2其他 , which is related to one or more of the luminance filter, spot shape filter, squared norm gradient, standard deviation, smoothing filter, energy filter based on gray level occurrence, homogeneity filter based on gray level occurrence, dissimilarity filter based on gray level occurrence, Lowe's energy filter, or threshold region filter, or a linear combination thereof by |ρ Ф2其他,Фm |≥0.40, where Ф m is one of the luminance filter, spot shape filter, squared norm gradient, standard deviation, smoothing filter, energy filter based on gray level occurrence, homogeneity filter based on gray level occurrence, dissimilarity filter based on gray level occurrence, Lowe's energy filter, or threshold region filter, or a linear combination thereof.

[0288] Example 32: The detector according to any one of Examples 25 to 31, wherein the material property m and / or the ordinate z is obtained by using determined according to a predetermined relationship between z and m.

[0289] Example 33: A detector according to any one of Examples 25 to 32, wherein the material property m and / or the ordinate z are / is determined by a function and / or determined.

[0290] Example 34: A detector according to any one of Examples 25 to 33, wherein the evaluation device is configured to apply a distance-related image filter and a material-related image filter to the reflected image simultaneously.

[0291] Example 35: A detector according to any one of Examples 25 to 34, wherein the evaluation device is configured to determine whether at least one of Ф1 or Ф2 is a characteristic or of another image filter, or whether at least one of Ф1 or Ф2 is a function of at least one other image filter, wherein the evaluation device is configured to apply the distance-related image filter and the material-related image filter to the reflected image sequentially or recursively.

[0292] Example 36: A detector according to the previous example, wherein the evaluation device is configured to determine at least one of z and / or m by applying at least one other filter that depends on and at least one of them to the reflected image.

[0293] Example 37: A detector according to any one of Examples 25 to 36, wherein the photon depth ratio filter includes: evaluating a combined signal Q of at least two sensor signals from a sensor element, wherein the evaluation device is configured to obtain the combined signal Q by dividing the sensor signals, dividing multiples of the sensor signals, dividing a linear combination of the sensor signals, or a combination of one or more of these, and wherein the evaluation device is configured to use at least one predetermined relationship between the combined signal Q and the distance characteristic to determine the distance characteristic

[0294] Example 38: A detector according to the previous example, wherein the evaluation device is configured to obtain the combined signal Q by

[0295]

[0296] where x and y are abscissas, A1 and A2 are different regions of the beam profile, and E(x, y, z o ) represents the object distance z oa given beam profile, wherein each of the sensor signals comprises at least one information of at least one region of the beam profile of the light beam propagating from the object to the detector.

[0297] Example 39: The detector according to the previous example, wherein the photosensitive regions are arranged such that the first sensor signal comprises information of a first region of the beam profile, and the second sensor signal comprises information of a second region of the beam profile, wherein the first region of the beam profile and the second region of the beam profile are one or both of adjacent or overlapping regions.

[0298] Example 40: The detector according to the previous example, wherein the evaluation means is configured to determine a first region of the beam profile and a second region of the beam profile, wherein the first region of the beam profile comprises substantially edge information of the beam profile, and the second region of the beam profile comprises substantially central information of the beam profile, wherein the edge information comprises information related to the number of photons in the first region of the beam profile, and the central information comprises information related to the number of photons in the second region of the beam profile.

[0299] Example 41: The detector according to any one of Examples 25 to 40, wherein the defocus depth filter comprises: using at least one convolution-based algorithm, such as a defocus depth algorithm, wherein the evaluation means is configured to determine the distance feature by optimizing at least one blur function f a to determine the distance feature wherein the blur function is optimized by changing the parameters of at least one blur function.

[0300] Example 42: The detector according to the previous example, wherein the reflected image is a blurred image i b , wherein the evaluation means is configured to reconstruct the distance feature from the blurred image i b and the blur function f a to reconstruct the distance feature wherein the blur function f is minimized by changing the parameter σ of the blur function a and at least one other image i' b of the convolution of and the blurred image i b the difference between, min‖(i′ b *f a (σ(z)) - i b )‖, to determine the distance feature

[0301] Example 43: The detector according to any one of Examples 25 to 42, wherein at least one blur function f aA function or composite function composed of at least one function from the group including the following: Gaussian function, sine function, parabolic cylinder function, square function, Lorentz function, radial function, polynomial, Hermite polynomial, Zernike polynomial, Legendre polynomial.

[0302] Example 44: A detector according to any one of Examples 25 to 43, wherein the sensor element includes at least one CMOS sensor.

[0303] Example 45: A detector according to any one of Examples 25 to 44, wherein the detector includes at least one irradiation source, wherein the irradiation source is configured to generate at least one irradiation pattern for irradiating an object, wherein the irradiation pattern includes at least one pattern selected from the group including the following: at least one dot pattern, in particular, a pseudo-random dot pattern; a random dot pattern or a quasi-random pattern; at least one Sobol pattern; at least one quasi-periodic pattern; at least one pattern including at least one known feature; at least one regular pattern; at least one triangular pattern; at least one hexagonal pattern; at least one rectangular pattern; at least one pattern including a convex uniform tiling; at least one line pattern including at least one line; at least one line pattern including at least two lines such as parallel lines or crossed lines.

[0304] Example 46: A detector according to the previous example, wherein the irradiation source includes at least one laser source and at least one diffractive optical element.

[0305] Example 47: A detector according to any one of the previous two examples, wherein the detector includes at least two sensor elements, each sensor element having a matrix of optical sensors, wherein at least one first sensor element and at least one second sensor element are located at different spatial positions, wherein the evaluation device is configured to select at least one image determined by the first sensor element or the second sensor element as a reflection image, and is configured to select at least one image determined by the other sensor element of the first sensor element or the second sensor element as a reference image.

[0306] Example 48: A detector according to the previous example, wherein the evaluation device is configured to select at least one reflection feature of the reflection image, wherein the evaluation device is configured to determine the distance feature At least one distance estimate of a selected reflection feature in a reflected image given by a sum error interval ±ε, wherein the evaluation device is configured to determine at least one displacement region in the reference image corresponding to the distance estimate, wherein the evaluation device is configured to match the selected reflection feature with at least one reference feature within the displacement region, wherein the evaluation device is configured to determine the displacement between the matched reference feature and the selected reflection feature, and wherein the evaluation device is configured to use a predetermined relationship between the ordinate and the displacement to determine the ordinate of the matched feature.

[0307] Example 49: A detector according to the previous example, wherein the reference image and the reflected image are images of an object determined at different spatial positions with a fixed distance, wherein the evaluation device is adapted to determine the epipolar line in the reference image, wherein the displacement region extends along the epipolar line, and wherein the evaluation device is adapted to determine the reference feature along the epipolar line corresponding to the distance feature and determine the range of the displacement region along the epipolar line corresponding to the error interval ±ε.

[0308] Example 50: A detector for identifying at least one material property m, comprising:

[0309] - At least one sensor element, which includes a matrix of optical sensors, each optical sensor having a photosensitive region, wherein the sensor element is configured to record at least one reflected image of a light beam originating from at least one object;

[0310] - At least one evaluation device, which is configured to determine the material property by evaluating at least one beam profile of the reflected image,

[0311] wherein the evaluation device is configured to determine at least one distance feature by applying at least one distance-related image filter Ф1 to the reflected image wherein the distance-related image filter is at least one filter selected from the group comprising: a photon depth ratio filter; a defocus depth filter; or a linear combination thereof; or is another distance-related image filter Ф 1其他 which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, wherein Ф z is one of the photon depth ratio filter or the defocus depth filter or a linear combination thereof,

[0312] wherein the evaluation device is configured to determine at least one material feature by applying at least one material-related image filter Ф2 to the reflected image

[0313] wherein the evaluation device is configured to determine the material property by evaluating the distance feature and material characteristics to determine the ordinate z and the material property m.

[0314] Example 51: A detector according to the previous example, wherein the material-related image filter is at least one filter by hypothesis testing, wherein the hypothesis testing uses the null hypothesis that the filter does not distinguish material classifiers and the alternative hypothesis that the filter distinguishes at least two material classifiers, and wherein the filter passes the hypothesis testing if the p-value p is less than or equal to a predefined significance level.

[0315] Example 52: A detector according to the previous example, wherein the hypothesis testing is based on a data set of beam profile images, and wherein each beam profile image corresponds to a material classifier and a distance.

[0316] Example 53: A detector according to any one of Examples 50 and 52, wherein p ≤ 0.075, preferably p ≤ 0.05, more preferably p ≤ 0.025, and most preferably p ≤ 0.01.

[0317] Example 54: A detector according to any one of Examples 50 to 53, wherein at least one material property is a property selected from the group consisting of: scattering coefficient, translucency, transparency, deviation from Lambertian surface reflection, speckle, and the like.

[0318] Example 55: A detector according to any one of Examples 50 to 54, wherein another distance-related image filter Ф 1其他 is related to one or more of the distance-related image filters Ф Ф1其他,Фz by |ρ z | ≥ 0.60, preferably |ρ Ф1其他,Фz | ≥ 0.80.

[0319] Example 56: A detector according to any one of Examples 50 to 55, wherein another material-related image filter Ф 2其他 is related to one or more of the material-related image filters Ф Ф2其他,Фm by |ρ m | ≥ 0.60, preferably |ρ Ф2其他,Фm | ≥ 0.80.

[0320] Example 57: A detector according to any one of Examples 50 to 56, wherein the material-related image filter is at least one filter selected from the group consisting of: a luminance filter; a spot shape filter; a squared norm gradient; a standard deviation; a smoothing filter such as a Gaussian filter or a median filter; a contrast filter based on gray-level occurrence; an energy filter based on gray-level occurrence; a homogeneity filter based on gray-level occurrence; a dissimilarity filter based on gray-level occurrence; a Lowe's energy filter; a threshold region filter; or a linear combination thereof; or is another material-related image filter Ф 2其他 , which is related to one or more of a luminance filter, a spot shape filter, a squared norm gradient, a standard deviation, a smoothing filter, an energy filter based on gray-level occurrence, a homogeneity filter based on gray-level occurrence, a dissimilarity filter based on gray-level occurrence, a Lowe's energy filter, or a threshold region filter, or a linear combination thereof by |ρ Ф2其他,Фm |≥0.40, where Ф m is one of a luminance filter, a spot shape filter, a squared norm gradient, a standard deviation, a smoothing filter, an energy filter based on gray-level occurrence, a homogeneity filter based on gray-level occurrence, a dissimilarity filter based on gray-level occurrence, a Lowe's energy filter, or a threshold region filter, or a linear combination thereof.

[0321] Example 58: A detector according to any one of Examples 50 to 57, wherein the material property m and / or the ordinate z are determined by using a predetermined relationship between z and m.

[0322] Example 59: A detector according to any one of Examples 50 to 58, wherein the material property m and / or the ordinate z are determined by a function and / or .

[0323] Example 60: A detector according to any one of Examples 50 to 59, wherein the evaluation device is configured to apply a distance-related image filter and a material-related image filter to the reflected image simultaneously.

[0324] Example 61: A detector according to any one of Examples 50 to 60, wherein the evaluation device is configured to determine whether at least one of Ф1 or Ф2 is a function of the features of other image filters or , or whether at least one of Ф1 or Ф2 is a function of at least one other image filter, wherein the evaluation device is configured to apply the distance-related image filter and the material-related image filter to the reflected image sequentially or recursively.

[0325] Example 62: The detector according to the previous example, wherein the evaluation means is configured to determine at least one of z and / or m by applying at least one further filter depending on and to the reflected image.

[0326] Example 63: The detector according to any one of Examples 50 to 62, wherein the photon depth ratio filter comprises evaluating a combined signal Q of at least two sensor signals from the sensor elements, wherein the evaluation means is configured to obtain the combined signal Q by dividing sensor signals, dividing multiples of sensor signals, dividing linear combinations of sensor signals, and wherein the evaluation means is configured to use at least one predetermined relationship between the combined signal Q and the distance feature to determine the distance feature

[0327] Example 64: The detector according to the previous example, wherein the evaluation means is configured to obtain the combined signal Q by

[0328]

[0329] where x and y are abscissas, A1 and A2 are different regions of the beam profile, and E(x, y, z o ) represents the beam profile given at the object distance z o wherein each of the sensor signals comprises at least one information of at least one region of the beam profile of the light beam propagating from the object to the detector.

[0330] Example 65: The detector according to the previous example, wherein the photosensitive regions are arranged such that the first sensor signal comprises information of the first region of the beam profile and the second sensor signal comprises information of the second region of the beam profile, wherein the first region of the beam profile and the second region of the beam profile are one or both of adjacent or overlapping regions.

[0331] Example 66: The detector according to the previous example, wherein the evaluation means is configured to determine the first region of the beam profile and the second region of the beam profile, wherein the first region of the beam profile comprises substantially edge information of the beam profile and the second region of the beam profile comprises substantially central information of the beam profile, wherein the edge information comprises information related to the number of photons in the first region of the beam profile and the central information comprises information related to the number of photons in the second region of the beam profile.

[0332] Example 67: A detector according to any one of Examples 50 to 66, wherein the defocus depth filter comprises: using at least one convolution-based algorithm, such as a defocus depth algorithm, wherein the evaluation means is configured to determine the distance feature by optimizing at least one blurring function f a to determine the distance feature wherein the blurring function is optimized by changing the parameters of at least one blurring function.

[0333] Example 68: A detector according to the previous example, wherein the reflected image is a blurred image i b , wherein the evaluation means is configured to reconstruct the distance feature from the blurred image i b and the blurring function f a to reconstruct the distance feature wherein the blurring function f is minimized by changing the parameter σ of the blurring function a and at least one other image i' b the difference between the convolution of and the blurred image i b , min ‖(i′ b *f a (σ(z)) - i b )‖, to determine the distance feature

[0334] Example 69: A detector according to any one of Examples 50 to 68, wherein at least one blurring function f a is a function or a composite function composed of at least one function from the group comprising: Gaussian function, sine function, parabolic cylinder function, square function, Lorentz function, radial function, polynomial, Hermite polynomial, Zernike polynomial, Legendre polynomial.

[0335] Example 70: A detector according to any one of Examples 50 to 69, wherein the sensor element comprises at least one CMOS sensor.

[0336] Example 71: A detector according to any one of Examples 50 to 70, wherein the detector comprises at least one irradiation source, wherein the irradiation source is configured to generate at least one irradiation pattern for irradiating an object, wherein the irradiation pattern comprises at least one pattern selected from the group comprising: at least one dot pattern, in particular a pseudo-random dot pattern; a random dot pattern or a quasi-random pattern; at least one Sobol pattern; at least one quasi-periodic pattern; at least one pattern comprising at least one known feature; at least one regular pattern; at least one triangular pattern; at least one hexagonal pattern; at least one rectangular pattern; at least one pattern comprising a protruding uniform tiling; at least one line pattern comprising at least one line; at least one line pattern comprising at least two lines such as parallel lines or intersecting lines.

[0337] Embodiment 72: The detector according to the previous embodiment, wherein the irradiation source includes at least one laser source and at least one diffractive optical element.

[0338] Embodiment 73: The detector according to any one of the previous two embodiments, wherein the detector includes at least two sensor elements, each sensor element having a matrix of optical sensors, wherein at least one first sensor element and at least one second sensor element are located at different spatial positions, wherein the evaluation means is configured to select at least one image determined by the first sensor element or the second sensor element as the reflected image, and is configured to select at least one image determined by the other sensor element of the first sensor element or the second sensor element as the reference image.

[0339] Embodiment 74: The detector according to the previous embodiment, wherein the evaluation means is configured to select at least one reflection feature of the reflected image, wherein the evaluation means is configured to determine at least one distance estimate of the selected reflection feature in the reflected image given by the distance feature and the error interval ±ε, wherein the evaluation means is configured to determine at least one displacement region in the reference image corresponding to the distance estimate, wherein the evaluation means is configured to match the selected reflection feature with at least one reference feature within the displacement region, wherein the evaluation means is configured to determine the displacement between the matched reference feature and the selected reflection feature, and wherein the evaluation means is configured to use a predetermined relationship between the ordinate and the displacement to determine the ordinate of the matched feature.

[0340] Embodiment 75: The detector according to the previous embodiment, wherein the reference image and the reflected image are images of an object determined at different spatial positions with a fixed distance, wherein the evaluation means is adapted to determine the epipolar line in the reference image, wherein the displacement region extends along the epipolar line, and wherein the evaluation means is adapted to determine the reference feature along the epipolar line corresponding to the distance feature and determine the range of the displacement region along the epipolar line corresponding to the error interval ±ε.

[0341] Embodiment 76: A detector system, the detector system including at least one detector according to any one of Embodiments 1 to 24, 25 to 49, or 50 to 75, the detector system further including at least one beacon device adapted to direct at least one light beam towards the detector, wherein the beacon device is at least one of attachable to an object, holdable by an object, and integratable into an object.

[0342] Example 77: A human-machine interface for exchanging at least one piece of information between a user and a machine, wherein the human-machine interface includes at least one detector system according to the previous embodiment, wherein at least one beacon device is adapted to be directly or indirectly attached to the user and at least one of being held by the user, wherein the human-machine interface is designed to determine at least one position of the user by means of the detector system, and wherein the human-machine interface is designed to assign at least one piece of information to the position.

[0343] Example 78: An entertainment device for performing at least one entertainment function, wherein the entertainment device includes at least one human-machine interface according to the previous embodiment, wherein the entertainment device is designed to enable a player to input at least one piece of information by means of the human-machine interface, and wherein the entertainment device is designed to change the entertainment function according to the information.

[0344] Example 79: A tracking system for tracking the position of at least one movable object, the tracking system includes at least one detector system according to any one of the foregoing embodiments related to the detector system, and the tracking system further includes at least one tracking controller, wherein the tracking controller is adapted to track a series of positions of the object at a specific time point.

[0345] Example 80: A scanning system for determining the depth profile of a scene, the scanning system includes at least one detector according to any one of Embodiments 1 to 24, 25 to 49, or 50 to 75, and the scanning system further includes at least one illumination light source adapted to scan the scene with at least one light beam.

[0346] Example 81: A camera for imaging at least one object, the camera includes at least one detector according to any one of Embodiments 1 to 24, 25 to 49, or 50 to 75.

[0347] Example 82: An inertial measurement unit used in an electronic device, wherein the inertial measurement unit is adapted to receive data determined by at least one detector according to any one of Embodiments 1 to 24, 25 to 49, or 50 to 75, and wherein the inertial measurement unit is further adapted to receive data determined by at least one other sensor, and the other sensors are selected from the group including the following: wheel speed sensor, steering rate sensor, tilt sensor, orientation sensor, motion sensor, magnetohydrodynamic sensor, force sensor, angle sensor, angular rate sensor, magnetic field sensor, magnetometer, accelerometer; gyroscope, and wherein the inertial measurement unit is adapted to determine at least one characteristic of the electronic device by evaluating data from the detector and at least one other sensor, and the at least one characteristic is selected from the group including the following: spatial position, relative or absolute motion in space, rotation, acceleration, orientation, angular position, tilt angle, steering rate, speed.

[0348] Example 83: A method for determining at least one material property of at least one object by using at least one detector according to any of the foregoing examples, the method comprising the following steps:

[0349] a) Determining at least one reflected image of the object by using at least one sensor element of a matrix with optical sensors, each optical sensor having a photosensitive area;

[0350] b) Determining the material property by evaluating at least one beam profile of the reflected image by using at least one evaluation device, wherein the evaluation includes:

[0351] b1) Determining at least one distance feature by applying at least one distance-related image filter Ф1 to the reflected image wherein the distance-related image filter is at least one filter selected from the group comprising: photon depth ratio filter; defocus depth filter; or a linear combination thereof; or is another distance-related image filter Ф 1其他 , which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, wherein Ф z is one of the photon depth ratio filter or the defocus depth filter or a linear combination thereof,

[0352] b2) Determining at least one material feature by applying at least one material-related image filter Ф2 to the reflected image wherein the material-related image filter is at least one filter selected from the group comprising: brightness filter; spot shape filter; squared norm gradient; standard deviation; smoothing filter, such as a Gaussian filter or a median filter; contrast filter based on gray level occurrence; energy filter based on gray level occurrence; homogeneity filter based on gray level occurrence; dissimilarity filter based on gray level occurrence; Lowe's energy filter; threshold region filter; or a linear combination thereof; or is another material-related image filter Ф 2其他 , which is related to one or more of the brightness filter, the spot shape filter, the squared norm gradient, the standard deviation, the smoothing filter, the energy filter based on gray level occurrence, the homogeneity filter based on gray level occurrence, the dissimilarity filter based on gray level occurrence, the Lowe's energy filter, or the threshold region filter or a linear combination thereof by |ρ Ф2其他,Фm |≥0.40, wherein Ф mis one of a luminance filter, a spot shape filter, a squared norm gradient, a standard deviation, a smoothing filter, an energy filter based on gray-level occurrence, a homogeneity filter based on gray-level occurrence, a dissimilarity filter based on gray-level occurrence, a Lowe energy filter, or a threshold region filter or a linear combination thereof,

[0353] b3) by evaluating the distance feature and the material feature to determine the ordinate z and the material property m.

[0354] Example 84: A method for determining at least one material property of at least one object by using at least one detector according to any one of Examples 25 to 49, the method comprising the following steps:

[0355] a) determining at least one reflected image of the object by using at least one sensor element of a matrix having an optical sensor, each optical sensor having a photosensitive area;

[0356] b) determining the material property by evaluating at least one beam profile of the reflected image by using at least one evaluation device, wherein the evaluation includes:

[0357] b1) determining at least one distance feature by applying at least one distance-related image filter Ф1 to the reflected image wherein the distance-related image filter is at least one filter selected from the group consisting of: a photon depth ratio filter; a defocus depth filter; or a linear combination thereof; or is another distance-related image filter Ф 1其他 , which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, wherein Ф z is one of a photon depth ratio filter or a defocus depth filter or a linear combination thereof,

[0358] b2) determining at least one material feature by applying at least one material-related image filter Ф2 to the reflected image wherein the material-related image filter is at least one filter by hypothesis testing, wherein the hypothesis testing uses a null hypothesis that the filter does not distinguish material classifiers and an alternative hypothesis that the filter distinguishes at least two material classifiers, wherein if the p-value p is less than or equal to a predefined significance level, the filter passes the hypothesis testing,

[0359] b3) by evaluating the distance feature and the material feature to determine the ordinate z and the material property m.

[0360] Example 85: A method for determining at least one material property of at least one object by using at least one detector according to any one of Examples 50 to 75, the method comprising the following steps:

[0361] a) Determining at least one reflected image of the object by using at least one sensor element of a matrix having an optical sensor, the optical sensors each having a photosensitive area;

[0362] b) Determining the material property by evaluating at least one beam profile of the reflected image by using at least one evaluation device, wherein the evaluation comprises:

[0363] b1) Determining at least one distance feature by applying at least one distance-dependent image filter Ф1 to the reflected image wherein the distance-dependent image filter is at least one filter selected from the group comprising: a photon depth ratio filter; a defocus depth filter; or a linear combination thereof; or is another distance-dependent image filter Ф 1其他 , which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, wherein Ф z is one of the photon depth ratio filter or the defocus depth filter or a linear combination thereof,

[0364] b2) Determining at least one material feature by applying at least one material-dependent image filter Ф2 to the reflected image

[0365] b3) Determining the ordinate z and the material property m by evaluating the distance feature and the material feature to determine the ordinate z and the material property m.

[0366] Example 86: Use of a detector according to any one of Examples 1 to 24, 25 to 49, or 50 to 75, for use purposes, the use being selected from the group comprising: position measurement in traffic technology; entertainment applications; security applications; surveillance applications; safety applications; human-machine interface applications; logistics applications; tracking applications; outdoor applications; mobile applications; communication applications; photographic applications; machine vision applications; robotic applications; quality control applications; manufacturing applications. Description of the Drawings

[0367] Other optional details and features of the present invention are apparent from the following description of preferred exemplary embodiments of the dependent claims. In this context, a particular feature may be implemented in isolation or in combination with other features. The present invention is not limited to the exemplary embodiments. The exemplary embodiments are schematically illustrated in the drawings. In the respective drawings, the same reference numerals refer to the same elements or elements having the same function, or elements corresponding to each other with respect to their function.

[0368] Specifically, in the drawings:

[0369] Figure 1 An embodiment of a detector according to the present invention is shown;

[0370] Figure 2 Statistics of the resulting material characteristics of an image filter related to a spot shape material applied to a data set are shown; and

[0371] Figure 3 A beam profile image is shown. Detailed Description

[0372] Figure 1 An embodiment of a detector 110 for identifying at least one material property m of at least one object 112 is shown in a highly schematic manner. The detector 110 includes at least one sensor element 116 of a matrix 118 having an optical sensor 120. Each optical sensor 120 has a photosensitive area 122.

[0373] The sensor element 116 may be formed as a single unitary device or as a combination of multiple devices. The matrix 118 may specifically be or may include a rectangular matrix having one or more rows and one or more columns. The rows and columns may specifically be arranged in a rectangular manner. However, other arrangements are also possible, such as a non-rectangular arrangement. As an example, a circular arrangement is also possible, where the elements are arranged in concentric circles or ellipses around a central point. For example, the matrix 118 may be a single row of pixels. Other arrangements are also possible.

[0374] The optical sensors 120 of matrix 118 may specifically be equal in one or more of size, sensitivity, and other optical, electrical, and mechanical characteristics. The photosensitive areas 122 of all the optical sensors 120 in matrix 118 may specifically be located in a common plane, which preferably faces the object 112, such that the light beams propagating from the object to the detector 110 may generate light spots on the common plane. The photosensitive areas 122 may specifically be located on the surfaces of the respective optical sensors 120. However, other embodiments are also feasible. The optical sensors 120 may include, for example, at least one CCD and / or CMOS device. As an example, the optical sensors 120 may be part of or constitute a pixelated optical device. As an example, the optical sensors 120 may be part of or constitute at least one CCD and / or CMOS device having a pixel matrix, with each pixel forming a photosensitive area 122.

[0375] The optical sensors 120 may specifically be or may include photodetectors, preferably inorganic photodetectors, more preferably inorganic semiconductor photodetectors, and most preferably silicon photodetectors. Specifically, the optical sensors 120 may be sensitive in the infrared spectral range. All the optical sensors 120 in matrix 118 or at least a group of optical sensors 120 in matrix 118 may specifically be the same. The groups of identical optical sensors 120 in matrix 118 may specifically be provided for different spectral ranges, or all the optical sensors may be the same in terms of spectral sensitivity. Additionally, the optical sensors 120 may be the same in size and / or with respect to their electronic or optoelectronic characteristics. Matrix 118 may be composed of independent optical sensors 120. Thus, a matrix 118 of inorganic photodiodes may be formed. However, alternatively, commercially available matrices such as one or more of CCD detectors (such as CCD detector chips) and / or CMOS detectors (such as CMOS detector chips) may be used.

[0376] The optical sensors 120 may form a sensor array or may be part of a sensor array, such as the above matrix. Thus, as an example, the detector 110 may include a pixel array, such as a rectangular array, having m rows and n columns, where m and n are independently positive integers. Preferably, more than one column and more than one row are given, i.e., n > 1, m > 1. Thus, as an example, n may be 2 to 16 or higher and m may be 2 to 16 or higher. Preferably, the ratio of the number of rows to the number of columns is close to 1. As an example, m and n may be chosen such that 0.3 ≤ m / n ≤ 3, such as by choosing m / n = 1:1, 4:3, 16:9, or similar values. As an example, the array may be a square array having the same number of rows and columns, such as by choosing m = 2, n = 2 or m = 3, n = 3, etc.

[0377] Matrix 118 can specifically be a rectangular matrix having at least one row (preferably multiple rows) and multiple columns. As an example, the rows and columns can be oriented substantially vertically. To provide a wide range of views, matrix 118 can particularly have at least 10 rows, preferably at least 50 rows, more preferably at least 100 rows. Similarly, the matrix can have at least 10 columns, preferably at least 50 columns, more preferably at least 100 columns. Matrix 118 can include at least 50 optical sensors 120, preferably at least 100 optical sensors 120, more preferably at least 500 optical sensors 120. Matrix 118 can include multiple pixels in the range of millions of pixels. However, other embodiments are feasible.

[0378] Detector 110 can further include an irradiation source 124. As an example, irradiation source 124 can be configured to generate an irradiation beam 126 for irradiating object 112. Detector 110 can be configured such that irradiation beam 126 propagates from detector 110 towards object 112 along the optical axis 128 of detector 110. For this purpose, detector 110 can include at least one reflecting element, preferably at least one prism, for deflecting the irradiation beam onto the optical axis 128.

[0379] Irradiation source 124 can include at least one light source. Irradiation source 124 can include multiple light sources. Irradiation source 124 can include an artificial irradiation source, specifically, at least one laser source and / or at least one incandescent lamp and / or at least one semiconductor light source, such as at least one light-emitting diode, specifically, organic and / or inorganic light-emitting diodes. As an example, the light emitted by irradiation source 124 can have a wavelength in the range of 300 to 1100 nm (specifically, 500 to 1100 nm). Additionally or alternatively, light in the infrared spectral range (such as in the range of 780 nm to 3.0 μm) can be used. Specifically, light in the near-infrared region, specifically in the range of 700 nm to 1100 nm, which is applicable to a part of silicon photodiodes, can be used. Using light in the near-infrared region makes the light undetectable or only weakly detectable by the human eye and still detectable by silicon sensors, especially standard silicon sensors. Irradiation source 124 can be adapted to emit light of a single wavelength. In other embodiments, the irradiation can be adapted to emit light having multiple wavelengths, thereby allowing additional measurements in other wavelength channels. The light source can be or can include at least one multi-beam light source. For example, the light source can include at least one laser source and one or more diffractive optical elements (DOEs).

[0380] Specifically, the illumination source 124 may include at least one laser and / or laser source. Various types of lasers may be employed, such as semiconductor lasers, double heterostructure lasers, external cavity lasers, separate confinement heterostructure lasers, quantum cascade lasers, distributed Bragg reflector lasers, polariton lasers, hybrid silicon lasers, extended cavity diode lasers, quantum dot lasers, bulk Bragg grating lasers, indium arsenide lasers, transistor lasers, diode-pumped lasers, distributed feedback lasers, quantum well lasers, interband cascade lasers, gallium arsenide lasers, semiconductor ring lasers, extended cavity diode lasers, or vertical cavity surface emitting lasers. Additionally or alternatively, non-laser light sources such as LEDs and / or light bulbs may be used. The illumination source 124 may include one or more diffractive optical elements (DOEs) adapted to generate an illumination pattern. For example, the illumination source 124 may be adapted to generate and / or project a point cloud. For example, the illumination source 124 may include one or more of at least one digital light processing projector, at least one LCoS projector, at least one spatial light modulator; at least one diffractive optical element; at least one light emitting diode array; at least one laser light source array. Considering their generally defined beam profiles and other operability characteristics, it is particularly preferred to use at least one laser source as the illumination source. The illumination source 124 may be integrated into the housing of the detector 110.

[0381] The illumination beam 126 may propagate from the object 112 towards the detector 110. The detector 110 may be used for active and / or passive illumination scenarios. For example, at least one illumination source 124 may be adapted to illuminate the object 112, e.g., by directing a beam towards the object 112, which reflects the beam. In addition to or alternatively to the at least one illumination source 124, the detector 110 may use radiation already present in the scene (such as from at least one ambient light source).

[0382] The illumination source 124 may illuminate at least one object 112 with at least one illumination pattern. The illumination pattern may include a plurality of points as image features. These points are shown as beams 126 emitted from the illumination source 124.

[0383] The detector 110 may include at least one transfer device 129, which includes one or more of the following: at least one lens, such as at least one lens selected from the group consisting of: at least one focus-adjustable lens, at least one aspherical lens, at least one spherical lens, at least one Fresnel lens; at least one diffractive optical element; at least one concave mirror; at least one beam deflection element, preferably at least one mirror; at least one beam splitting element, preferably at least one of a beam splitting cube or a beam splitting mirror; at least one multi-lens system. In particular, the transfer device 129 may include at least one collimating lens, which is adapted to focus at least one object point in the image plane.

[0384] Each optical sensor 120 may be designed to generate at least one sensor signal in response to illumination of its respective photosensitive area 122 by reflected light beam 130 propagating from object 112 to detector 110. The sensor element 116 may be configured to determine at least one reflected image of at least one reflection feature generated by the reflected light beam 130 on the optical sensor 120. The reflected image may be data recorded by using the sensor element 116, such as a plurality of electronic readings from an imaging device, such as the pixels of the sensor element 116. The reflected image itself may include pixels, and the pixels of the image are related to the pixels of the matrix 118 of the sensor element 116. The matrix 118 may include the reflected image 126. For example, in the case of illumination with a dot pattern, the reflected image may include dots as the reflection feature. These dots may be produced by the reflected light beam 130 originating from at least one object 112.

[0385] The sensor element 116 can be configured to record a beam profile of at least one reflection feature of the reflected image. The detector 110 includes at least one evaluation device 132. The evaluation device 132 can be configured to identify and / or select at least one reflection feature in the reflected image provided by the sensor element 116, specifically, at least one light spot. The evaluation device 132 can be configured to perform at least one image analysis and / or image processing in order to identify the reflection feature. The image analysis and / or image processing can use at least one feature detection algorithm. The image analysis and / or image processing can include one or more of the following: filtering; selecting at least one region of interest; forming a difference image between the image generated from the sensor signal and at least one offset; inverting the sensor signal by inverting the image generated from the sensor signal; forming a difference image between the images generated from the sensor signals at different times; background correction; decomposition into color channels; decomposition into hue, saturation, and brightness channels; frequency decomposition; singular value decomposition; applying a light spot detector; applying a corner detector; applying the determinant of the Hessian filter; applying a region detector based on principal curvature; applying a maximally stable extremal region detector; applying a generalized Hough transform; applying a ridge detector; applying an affine invariant feature detector; applying an affine adapted interest point operator; applying a Harris affine region detector; applying a Hessian affine region detector; applying a scale invariant feature transform; applying a scale space extremum detector; applying a local feature detector; applying an accelerated robust features algorithm; applying a histogram of oriented gradient positions and orientations algorithm; applying a histogram of oriented gradient descriptors; applying a Deriche edge detector; applying a differential edge detector; applying a spatio-temporal interest point detector; applying a Moravec corner detector; applying a Canny edge detector; applying the Laplacian of the Gaussian filter; applying a difference of Gaussian filter; applying a Sobel operator; applying a Laplacian operator; applying a Scharr operator; applying a Prewitt operator; applying a Roberts operator; applying a Kirsch operator; applying a high-pass filter; applying a low-pass filter; applying a Fourier transform; applying a Radon transform; applying a Hough transform; applying a wavelet transform; threshold conversion method; creating a binary image. Specifically, the evaluation of the reflected image includes selecting a region of interest in the reflected image. The region of interest can be determined manually by the user or can be determined automatically, such as by discerning an object within the image generated by the sensor element 116. For example, in the case of a punctiform reflection feature, the region of interest can be selected as the region around the light spot profile.

[0386] The evaluation device 132 may be configured to determine the material property m by evaluating the beam profile of the reflected image. The beam profile of the reflected image may be selected from the group including the following: trapezoidal beam profile; triangular beam profile; conical beam profile, and a linear combination of Gaussian beam profiles. The evaluation device 132 may be configured to apply at least one distance-related image filter and at least one material-related image filter to the beam profile and / or at least one specific region of the beam profile. Specifically, the image filter Ф maps the image f or the region of interest in the image to a real number, where represents a feature. In particular, the feature is a distance feature in the case of a distance-related image filter and a material feature in the case of a material-related image filter. The image may be subject to noise, and so may the feature. Therefore, the feature may be a random variable. The feature may be normally distributed. If the feature is not normally distributed, they may be transformed to a normal distribution, such as by a Box-Cox transformation.

[0387] The evaluation device 132 is configured to determine at least one distance feature by applying at least one distance-related image filter Ф1 to the reflected image The distance feature may be or may include at least one piece of information about the distance of the object 112, such as at least one measure of the distance of the object 112, a distance value, the ordinate of the object, etc. The distance-related image filter is at least one filter selected from the group including the following: photon depth ratio filter; defocus depth filter; or a linear combination thereof; or is another distance-related image filter Ф 1其他 , which is related to the photon depth ratio filter and / or the defocus depth filter or a linear combination thereof by |ρ Ф1其他,Фz |≥0.40, where Ф z is one of the photon depth ratio filter or the defocus depth filter or a linear combination thereof. Another distance-related image filter Ф 1其他 may be related to one or more of the distance-related image filters Ф Ф1其他,Фz by |ρ Ф1其他,Фz |≥0.60, preferably by |ρ z |≥0.80. The similarity between the two image filters Ф i and Ф j may be evaluated by the correlation of their features. Specifically, by calculating the Pearson correlation coefficient,

[0388]

[0389] where μ and σ are the mean and standard deviation of the obtained features. A set of random test images, specifically, a matrix filled with random numbers, can be used to perform the test of filter correlation. The number of random test images can be selected such that the result of the correlation test is statistically significant. The correlation coefficient takes values between -1 and 1, and 0 indicates no linear correlation. The correlation coefficient is very suitable for determining whether two filters are similar or even equivalent. To measure whether the features of a filter are related to a given property (such as distance), the test images can be selected such that the relevant filter actually produces that property. As an example, to measure whether the features of a filter are related to distance, the beam profiles recorded at different distances can be used as test images. To obtain a comparable, transferable, and transparent evaluation, a fixed test set of test images can be defined.

[0390] For example, a distance-related image filter can be a photon depth ratio filter. The photon depth ratio filter can include evaluating a combined signal Q of at least two sensor signals from a sensor element. The evaluation device can be configured to determine a distance feature by evaluating the combined signal Q. The distance feature determined by evaluating the combined signal Q can directly correspond to the ordinate of the object. The combined signal Q can be determined by using various devices. As an example, a software device for obtaining the combined signal, a hardware device for obtaining the combined signal, or both can be used and implemented in the evaluation device. Thus, as an example, the evaluation device 132 can include at least one divider 134, where the divider 134 is configured to obtain a quotient signal. The divider 134 can be embodied as one or both of a software divider or a hardware divider, either in whole or in part.

[0391] The evaluation device 132 can be configured to obtain the combined signal Q by dividing one or more of the sensor signals, multiples of the sensor signals, or linear combinations of the sensor signals. The evaluation device 132 can be configured to use at least one predetermined relationship between the combined signal Q and the distance feature to determine the distance feature. For example, the evaluation device is configured to obtain the combined signal Q by the following formula:

[0392]

[0393] where x and y are the abscissas, A1 and A2 are different regions of at least one beam profile of the beam propagated from the object to the detector at the sensor position, and E(x, y, z o ) represents the object distance z. oThe beam profile given herein. Regions A1 and A2 may be different. Specifically, A1 and A2 are not congruent. Thus, A1 and A2 may be different in one or more of shape or content. The beam profile may be the cross-section of a light beam. The beam profile may be selected from the group including the following: trapezoidal beam profile; triangular beam profile; conical beam profile and a linear combination of Gaussian beam profiles. Generally, the beam profile depends on the luminance L(z o ) and the beam shape S(x, y; z o ). Thus, by deriving the combined signal, it is possible to determine the ordinate independently of the luminance. Additionally, using the combined signal allows the distance z o to be determined independently of the object size. Thus, the combined signal allows the distance z to be determined independently of the material properties and / or reflection properties and / or scattering properties of the object 112 and independently of changes in the light source (such as through manufacturing precision, heat, water, dirt, damage, etc. on the lens). o . As an example, the distance-related feature may be a function of the combined signal Q, and this function may be a linear, quadratic or higher-order polynomial in Q. Further, as an example, the object distance z0 may be a function of the distance-related feature , and this function may be a linear, quadratic or higher-order polynomial in . Thus, the object distance z0 may be a function of the combined signal Q, z0 = z0(Q), and this function may be a linear, quadratic or higher-order polynomial in Q.

[0394] The photosensitive regions 122 of at least two optical sensors 120 may be arranged such that the first sensor signal includes information on a first region of the beam profile and the second sensor signal includes information on a second region of the beam profile. The first region of the beam profile and the second region of the beam profile are one or both of adjacent or overlapping regions.

[0395] The evaluation device 132 may be configured to determine and / or select a first region of the beam profile and a second region of the beam profile. The first region of the beam profile may include substantially edge information of the beam profile, and the second region of the beam profile may include substantially central information of the beam profile. The beam profile may have a center, i.e., the center point of the maximum value of the beam profile and / or the center point of the plateau of the beam profile and / or the geometric center of the light spot, and have a descending edge extending from the center. The second region may include the inner region of the cross section, while the first region may include the outer region of the cross section. Preferably, the central information has a proportion of edge information of less than 10%, more preferably less than 5%, and most preferably, the central information does not include edge content. The edge information may include information of the entire beam profile particularly from the central and edge regions. The edge information may have a proportion of central information of less than 10%, preferably less than 5%, and more preferably, the edge information does not include central content. If at least one region of the beam profile is close to or around the center and includes substantially central information, at least one region of the beam profile may be determined and / or selected as the second region of the beam profile. If at least one region of the beam profile includes at least a part of the descending edge of the cross section, at least one region of the beam profile may be determined and / or selected as the first region of the beam profile. For example, the entire region of the cross section may be determined as the first region. The first region of the beam profile may be region A2, and the second region of the beam profile may be region A1.

[0396] The edge information may include information related to the number of photons in the first region of the beam profile, while the central information may include information related to the number of photons in the second region of the beam profile. The evaluation device 132 may be adapted to determine the area integral of the beam profile. The evaluation device 132 may be adapted to determine the edge information by integrating and / or summing the first region. The evaluation device 132 may be adapted to determine the central information by integrating and / or summing the second region. For example, the beam profile may be a trapezoidal beam profile, and the evaluation device may be adapted to determine the integral of the trapezoid. In addition, when a trapezoidal beam profile can be assumed, the determination of the edge and central signals may be replaced by an equivalent evaluation that utilizes the characteristics of the trapezoidal beam profile, such as determining the inclination and position of the edge and the height of the central plateau, and deriving the edge and central signals through geometric considerations.

[0397] Additionally or alternatively, the evaluation device may be adapted to determine one or both of the central information or the edge information from at least one slice or incision of the light spot. For example, this may be achieved by replacing the area integral in the combined signal Q with a line integral along the slice or incision. To improve accuracy, several slices or incisions through the light spot may be used and averaged. In the case of an elliptical light spot profile, averaging over multiple slices or incisions may result in improved distance information.

[0398] The evaluation device 132 may be configured to derive a combined signal Q by one or more of the following: dividing edge information and center information, dividing a multiple of edge information and center information, dividing a linear combination of edge information and center information. Thus, basically, the photon ratio can be used as the physical basis of this method.

[0399] The evaluation device 132 may specifically be configured to derive a combined signal Q by dividing the first and second sensor signals, dividing a multiple of the first and second sensor signals, or dividing a linear combination of the first and second sensor signals. As an example, Q may simply be determined as Q = s1 / s2 or Q = s2 / s1, where s1 represents the first sensor signal and s2 represents the second sensor signal. Additionally or alternatively, Q may be determined as Q = a·s1 / b·s2 or Q = b·s2 / a·s1, where a and b are real numbers, which may be predefined or determinable as an example. Additionally or alternatively, Q may be determined as Q = (a·s1 + b·s2) / (c·s1 + d·s2), where a, b, c, and d are real numbers, which are predefined or determinable as an example. As a simple example of the latter, Q may be determined as Q = s1 / (s1 + s2). Other combined signals or quotient signals are also feasible.

[0400] For further details and embodiments regarding the evaluation of the combined signal Q and the determination of the ordinate z, reference may be made, for example, to WO 2018 / 091640, WO 2018 / 091649 A1, and WO 2018 / 091638 A2, the entire disclosures of which are incorporated herein by reference.

[0401] For example, the distance-dependent image filter may be a depth-of-field filter. As outlined above, the evaluation device 132 may be configured to determine at least one image of the region of interest from the sensor signals. The evaluation device 132 may be configured to determine the distance feature of the object from the image by optimizing at least one blur function f a to determine the distance feature of the object from the image The determined distance feature may directly correspond to the ordinate of the object. The distance feature may be determined by using at least one convolution-based algorithm (such as the depth-of-field algorithm). To obtain the distance from the image, the depth-of-field algorithm estimates the defocus of the object. For this estimation, a blur function is assumed. Specifically, the blur function models the blur of the defocused object. The at least one blur function f a may be a function or a composite function composed of at least one function from the group including the following: Gaussian function, sine function, parabolic cylinder function, square function, Lorentz function, radial function, polynomial, Hermite polynomial, Zernike polynomial, Legendre polynomial.

[0402] The ambiguity function can be optimized by changing the parameters of at least one ambiguity function. The reflected image can be the blurred image i b . The evaluation device can be configured to reconstruct the distance feature from the blurred image i b and the ambiguity function f a to reconstruct the distance feature The distance feature can be determined by changing the parameters σ of the ambiguity function, by minimizing the convolution of the ambiguity function f a and at least one other image i' b and the blurred image i b The difference between, min‖(i′ b *f a (σ(z)) - i b )‖, to determine the distance feature σ(z) is a set of distance-related ambiguity parameters. The other image may be blurred or clear. At least one other image can be generated from the blurred image i by convolution with a known ambiguity function. Therefore, the depth of defocus algorithm can be used to obtain the distance feature b generate at least one other image. Therefore, the depth of defocus algorithm can be used to obtain the distance feature

[0403] The evaluation device 132 can be configured to determine at least one combined distance information z taking into account the distance feature determined by applying the photon depth ratio filter and the distance feature determined by applying the depth of defocus filter . The combined distance information z can be a real function that depends on the distance feature determined by applying the photon depth ratio filter and the distance feature determined by applying the depth of defocus filter The combined distance information z can be the distance feature determined by applying the photon depth ratio filter and the distance feature determined by applying the depth of defocus filter is a rational or irrational polynomial. The depth of defocus is a complementary method to the photon depth ratio, but uses a similar hardware setup. In addition, the depth of defocus distance measurement may have similar accuracy. Combining these two techniques can produce favorable distance measurement results with higher accuracy.

[0404] For example, a distance-related image filter can be a structured light filter combined with a photon depth ratio filter and / or a defocus depth image filter. The detector 110 can include at least two sensor elements 116, each sensor element 116 having a matrix 118 of optical sensors 120. At least one first sensor element and at least one second sensor element can be located at different spatial positions. The relative distance between the first sensor element and the second element can be fixed. At least one first sensor element can be adapted to determine at least one first reflection pattern, specifically, at least one first reflection feature, and at least one second sensor element can be adapted to determine at least one second reflection pattern, specifically, at least one second reflection feature. The evaluation device 132 can be configured to select at least one image determined by the first sensor element or the second sensor element as a reflection image, and be configured to select at least one image determined by the other sensor element of the first sensor element or the second sensor element as a reference image. The reference image can be determined by one or more of recording at least one reference feature, imaging at least one reference feature, and calculating the reference image. The reference image and the reflection image can be object images determined at different spatial positions with a fixed distance. The distance can be a relative distance, also known as a baseline. The evaluation device 132 can be adapted to select at least one reflection feature in the reflection image and determine at least one distance estimate of the selected reflection feature in the reflection image, the at least one distance estimate being given by distance features determined by applying a photon depth ratio image filter and / or a defocus depth image filter and an error interval ±ε.

[0405] The evaluation device 132 may be adapted to determine at least one reference feature corresponding to at least one reflection feature in at least one reference image. The evaluation device 132 may be adapted to perform image analysis and identify features in the reflection image. The evaluation device 132 may be adapted to identify at least one reference feature in the reference image that has a substantially identical ordinate to the selected reflection feature. The evaluation device 132 may be adapted to determine the epipolar line in the reference image. The relative position of the reference image and the reflection image may be known. For example, the relative position of the reference image and the reflection image may be stored in at least one storage unit of the evaluation device. The evaluation device 132 may be adapted to determine a straight line extending from the selected reflection feature of the reflection image. The straight line may include possible object features corresponding to the selected feature. The straight line and the baseline span the epipolar plane. Since the reference image is determined at a different relative position from the reflection image, the corresponding possible object features may be imaged on the straight line (referred to as the epipolar line) in the reference image. Therefore, the feature of the reference image corresponding to the selected feature of the reflection image lies on the epipolar line. Due to image distortion or changes in system parameters, such as due to aging, temperature changes, mechanical stress, etc., the epipolar lines may intersect or be very close to each other and / or the correspondence between the reference feature and the reflection feature may be unclear. In addition, each known position or object in the real world can be projected onto the reference image and vice versa. Due to the calibration of the detector, the projection may be known, and the calibration is comparable to the teachings of the epipolar geometry of a specific camera.

[0406] The evaluation device 132 may be configured to determine at least one displacement region in the reference image corresponding to the distance estimate. Specifically, the displacement region may be a region in the reference image in which the reference feature corresponding to the selected reflection feature is expected to be located in the reference image. Depending on the distance to the object 112, the image position of the reference feature corresponding to the reflection feature may be shifted within the reference image compared to the image position of the reflection feature in the reflection image. The displacement region may include only one reference feature. The displacement region may also include more than one reference feature. The displacement region may include an epipolar line or a part of an epipolar line. The displacement region may include more than one epipolar line or multiple parts of more than one epipolar line. The displacement region may extend along the epipolar line, be orthogonal to the epipolar line, or both. The evaluation device 132 may be adapted to determine the reference feature along the epipolar line corresponding to the distance feature and determine the range of the displacement region along the epipolar line corresponding to the error interval ±ε or orthogonal to the epipolar line. The measurement uncertainty of the distance estimate may result in a non-circular displacement region because the measurement uncertainties in different directions may be different. Specifically, the measurement uncertainty along one or more epipolar lines may be greater than the measurement uncertainty in the direction orthogonal to one or more epipolar lines. The displacement region may include an extension in the direction orthogonal to one or more epipolar lines. The evaluation device 132 may determine the displacement region around the image position of the reflection feature. The evaluation device 132 may be adapted to determine the distance estimate and determine the displacement region along the epipolar line corresponding to the corresponding one along the epipolar line.

[0407] The evaluation device 132 may be configured to match selected features in the reflection pattern with at least one feature of the reference pattern within the displacement region. The evaluation device 132 may be configured to match the selected features in the reflection image with the reference features within the displacement region by using at least one evaluation algorithm that takes into account the determined distance estimate. The evaluation algorithm may be a linear scaling algorithm. The evaluation device 132 may be adapted to determine the epipolar line closest to and / or within the displacement region. The evaluation device 132 may be adapted to determine the epipolar line of the image position closest to the reflection feature. The extent of the displacement region along the epipolar line may be greater than the extent of the displacement region orthogonal to the epipolar line. The evaluation device may be adapted to determine the epipolar line before determining the corresponding reference feature. The evaluation device 132 may determine a displacement region around the image position of each reflection feature. The evaluation device 132 may be adapted to assign an epipolar line to each displacement region of each image position of the reflection feature, such as by assigning the epipolar line closest to and / or within the displacement region and / or closest to the displacement region along the direction orthogonal to the epipolar line. The evaluation device 132 may be adapted to determine the reference feature corresponding to the image position of the reflection feature by determining the reference feature closest to and / or within the assigned displacement region and / or closest to the assigned displacement region along the assigned epipolar line and / or within the assigned displacement region along the assigned epipolar line.

[0408] The evaluation device 132 may be configured to determine the displacement of the matched reference feature and the selected reflection feature. The evaluation device 132 may be configured to use a predetermined relationship between the ordinate and the displacement to determine the longitudinal information of the matched feature. For example, the longitudinal information may be a distance value. The predetermined relationship may be one or more of an empirical relationship, a semi-empirical relationship, and a relationship obtained by analysis. The evaluation device 132 may include at least one data storage device for storing the predetermined relationship (such as a look-up list or a look-up table). The evaluation device 132 may be adapted to determine the predetermined relationship by using a triangulation method. In the case where the positions of the selected reflection feature and the matched reference feature in the known reflection image and / or the relative displacement between the selected reflection feature and the matched reference feature are known, the ordinate of the corresponding object feature may be determined by triangulation. Therefore, the evaluation device 132 may be adapted to select, for example, subsequent and / or column-by-column reflection features and use triangulation to determine the corresponding distance value for each potential position of the reference feature. The displacement and the corresponding distance value may be stored in at least one storage device of the evaluation device 132.

[0409] Additionally or alternatively, the evaluation device 132 may be configured to perform the following steps:

[0410] - Determine a displacement region for each image position of the reflection feature;

[0411] -assigning epipolar lines to the displacement regions of each reflection feature, such as by assigning epipolar lines closest to and / or within the displacement region and / or along the epipolar line closest to the displacement region and orthogonal to the epipolar line;

[0412] -assigning and / or determining at least one reference feature to each reflection feature, such as by assigning reference features closest to and / or within the assigned displacement region and / or along the assigned epipolar line closest to and / or within the assigned displacement region along the assigned epipolar line.

[0413] The evaluation device 132 is configured to determine at least one material feature by applying at least one material-related image filter Ф2 to the reflection image The material feature may be or may include at least one piece of information about at least one material property of the object 112.

[0414] The material-related image filter may be at least one filter selected from the group including the following: brightness filter; spot shape filter; squared norm gradient; standard deviation; smoothing filter, such as a Gaussian filter or a median filter; contrast filter based on gray level occurrence; energy filter based on gray level occurrence; homogeneity filter based on gray level occurrence; dissimilarity filter based on gray level occurrence; Lowe's energy filter; threshold region filter; or a linear combination thereof; or another material-related image filter Ф 2其他 , which is related to one or more of a brightness filter; spot shape filter; squared norm gradient; standard deviation; smoothing filter; energy filter based on gray level occurrence; homogeneity filter based on gray level occurrence; dissimilarity filter based on gray level occurrence; Lowe's energy filter; or threshold region filter; or a linear combination thereof by |ρ Ф2其他,Фm |≥0.40, where Ф m is one of a brightness filter; spot shape filter; squared norm gradient; standard deviation; smoothing filter; energy filter based on gray level occurrence; homogeneity filter based on gray level occurrence; dissimilarity filter based on gray level occurrence; Lowe's energy filter; or threshold region filter; or a linear combination thereof. Another material-related image filter Ф 2其他 is related to the material-related image filter Ф by |ρ Ф2其他,Фm |≥0.60, preferably by |ρ Ф2其他,Фm |≥0.80. mis related to one or more of them. For the description of exemplary material-related image filters, reference can be made to the descriptions of the luminance filter, spot shape filter, squared norm gradient, standard deviation, smoothing filter (such as Gaussian filter or median filter), contrast filter based on gray level occurrence, energy filter based on gray level occurrence, homogeneity filter based on gray level occurrence, dissimilarity filter based on gray level occurrence, Lowe's energy filter, and threshold region filter given above.

[0415] The material-related image filter can be at least one arbitrary filter Φ through hypothesis testing. As used herein, hypothesis testing can include testing the material relevance of an image filter by applying the image filter to a predefined data set. The data set can include a plurality of beam profile images. The beam profile images can be given by the sum of Gaussian radial basis functions, B wherein,

[0416]

[0417] where N B Each of the Gaussian radial basis functions is defined by a center (x lk , y lk ), a pre-factor a lk and an exponential factor ɑ = 1 / ∈. The exponential factors of all the Gaussian functions in all the beam profile images are the same. The center positions x k : of all the images f lk , y lk are the same. The above formula of f k (x, y) can be used in combination with the following parameter table to generate beam profile images

[0418]

[0419] The values of x and y are integers corresponding to the pixels with . The image can have a pixel size of 32x32. The above formula of f k can be used in combination with a parameter set to obtain a continuous description of f k to generate a data set of beam profile images. The value of each pixel in the 32x32 image can be obtained by inserting integer values from 0,..., 31 for x and y in f k (x, y). For example, for the pixel (6, 9), the value f k (6, 9) can be calculated. Figure 3 Shows an embodiment of a beam profile image defined by an exemplary data set.

[0420] An exemplary data set is presented below. The following parameter table lists all the images fk All Gaussian functions g lk The center position, x lk = x l , y lk = y l :

[0421]

[0422]

[0423] The following parameter table lists the material classifiers referenced by the image index k. In particular, for white Teflon targets (represented as controls), fabrics, dark skin (represented as dark_skin), pale skin (represented as pale_skin), and highly translucent skin (represented as translucent), and for each image f k The distance z:

[0424]

[0425]

[0426]

[0427] The following parameter table lists each image f referenced by the image index k and the Gaussian index l k The prefactor alk of the Gaussian function g lk in

[0428]

[0429]

[0430]

[0431]

[0432]

[0433]

[0434]

[0435]

[0436]

[0437]

[0438]

[0439]

[0440]

[0441]

[0442]

[0443]

[0444]

[0445]

[0446]

[0447]

[0448]

[0449]

[0450]

[0451]

[0452]

[0453]

[0454]

[0455]

[0456]

[0457]

[0458]

[0459]

[0460]

[0461]

[0462]

[0463]

[0464]

[0465]

[0466]

[0467]

[0468]

[0469]

[0470] For all images f k of all Gaussian functions g lk , the exponential factor α = 1 / ∈ is given by ε = 2.0615528128088303.

[0471] Subsequently, for each image f k , the eigenvalues corresponding to the filter Φ can be calculated where z k is the distance value corresponding to the image f k from a predefined data set. This results in a data set with the corresponding generated eigenvalues . A hypothesis test can use the null hypothesis that the filter does not describe the material classifier. The null hypothesis can be given by H0: μ1 = μ2 = … = μ J where μ m is the expected value for each material group corresponding to the eigenvalues . A hypothesis test can use the alternative hypothesis that the filter does describe the material classifier. The alternative hypothesis can be given by H1: μ m ≠ μ m′ . A hypothesis test can include at least one analysis of variance (ANOVA) on the generated eigenvalues. In particular, a hypothesis test can include determining the mean of the eigenvalues for each material, i.e., a total of J means, where m ∈ [0, 1, …, J - 1], where N m gives the number of eigenvalues of the material for each of the J materials in the predefined data set. A hypothesis test can include determining the mean of all eigenvalues A hypothesis test can include determining the sum of mean squares within the following range:

[0472]

[0473] A hypothesis test can include determining the sum of mean squares between the following,

[0474]

[0475] A hypothesis test can include performing an F-test:

[0476] □ where d1 = N - J, d2 = J - 1,

[0477] □F(x) = 1 - CDF(x)

[0478] □p = F(mssb / mssw)

[0479] In this article, I is the regular incomplete beta function, where the Euler beta function and are the incomplete beta functions. If the p - value p is less than or equal to a predefined significance level, the image filter can pass the hypothesis test. If p ≤ 0.075, preferably p ≤ 0.05, more preferably p ≤ 0.025, and most preferably p ≤ 0.01, the filter can pass the hypothesis test. For example, if the p - value is less than α = 0.05, the image filter can pass the hypothesis test. In this case, the null hypothesis H0 can be rejected, and the alternative hypothesis H1 can be accepted. The image filter thus differentiates at least two material classifiers. Thus, the image filter passes the hypothesis test.

[0480] Figure 2 Shows the experimental results of calculating the material characteristics of different material groups using the spot - shape filter. Specifically, the material characteristics are represented for different materials, i.e., from left to right as wooden board, fabric, dark skin, pale skin, highly translucent skin represented as semi - transparent, and shows the separation of the skin material (right) from other materials (left). The spot - shape filter passed the hypothesis test. The p - value calculated from the F - test is 0.0076. Thus, with respect to a significance level of 0.01, the null hypothesis H0 can be rejected. Additionally, with respect to a significance level of 0.01, the alternative hypothesis H1 can be accepted. Thus, the image filter differentiates at least two material classifiers. Additionally, Figure 2 shows the mean value of the characteristics per material as the line within the corresponding box. The vertical bars mark Q1 - 1.5 inter - quartile range (IQR) and Q3 + 1.5 IQR, where IQR = Q3 - Q1, and Q1 and Q3 are the first and third quartiles. The box marks the Q1 and Q3 quartiles. Outliers are plotted as points. Figure 2 Shows that the material characteristics separate all skin samples from non - skin samples. In other words, the expected values of the skin characteristics of skin and non - skin materials are different.

[0481] Additionally, Figure 1An exemplary embodiment of the detector 110 is shown in a highly schematic illustration, where the detector 110 can specifically be embodied as a camera 136 and / or can be part of the camera 136. The camera 136 can be manufactured for imaging, specifically for 3D imaging, and can be manufactured for acquiring still images and / or image sequences, such as digital video clips. Other embodiments are feasible. Figure 1 An embodiment of the detector system 138 is further shown, which, in addition to at least one detector 110, includes one or more beacon devices 140. In this example, the beacon devices can be attached and / or integrated into the object 112, and the position of the object 112 will be detected by using the detector 110. Figure 1 An exemplary embodiment of the human-machine interface 142 is further shown, which includes at least one detector system 138; and an entertainment device 144 is further shown, which includes the human-machine interface 142. The figure further shows an embodiment of the tracking system 146 for tracking the position of the object 112, which includes the detector system 138. The components of the devices and systems will be further explained in detail below.

[0482] Figure 1 An exemplary embodiment of the scanning system 148 for scanning a scene including the object 112 (such as for scanning the object 112 and / or for determining at least one position of at least one object 112) is further shown. The scanning system 148 includes at least one detector 110, and further optionally includes at least one irradiation source 124, and optionally includes at least one other irradiation source (not described here). The irradiation source 124 is generally configured to emit at least one irradiation beam, such as for irradiating at least one point, which is, for example, a point located at one or more positions of the beacon device 140 and / or on the surface of the object 112. The scanning system 148 can be designed to generate a contour of the scene including the object 112 and / or a contour of the object 112, and / or can be designed to generate at least one item of information about the distance between at least one point and the scanning system 148 (specifically, the detector 110) by using at least one detector 110.

[0483] In addition to the optical sensor 120, the detector 110 includes at least one evaluation device 132, which has, for example, at least one image analysis device 150 and / or at least one position evaluation device 152, as Figure 1is symbolically depicted. The components of the evaluation device 132 can be fully or partially integrated into different devices and / or can be fully or partially integrated into other components of the detector 110. In addition to the possibility of fully or partially combining two or more components, one or more of the optical sensors in the optical sensor 120 and one or more of the components of the evaluation device 132 can be interconnected with each other via one or more connectors 154 and / or via one or more interfaces, as Figure 1 is symbolically depicted. In addition, one or more of the connectors 154 can include one or more drivers and / or one or more devices for modifying or preprocessing the sensor signals. In addition, instead of using at least one optional connector 154, the evaluation device 132 can be fully or partially integrated into one or both of the optical sensors 120 and / or integrated into the housing 156 of the detector 110. Additionally or alternatively, the evaluation device 132 can be fully or partially designed as a separate device.

[0484] In this exemplary embodiment, the object 112 whose position can be detected can be designed as an item of sports equipment and / or can form a control element or control device 158, the position of which can be manipulated by the user 160. As an example, the object 112 can be or can include a bat, a racket, a club, or any other sports equipment and / or imitation sports equipment. Other types of objects 112 are possible. In addition, the user 160 himself or herself can be considered as the object 112, the position of which will be detected.

[0485] As outlined above, the detector 110 includes the optical sensor 120. The optical sensor 120 can be located inside the housing 156. In addition, the detector 110 can include at least one transfer device 129, such as one or more optical systems, preferably including one or more lenses. The opening 162 inside the housing 156, which is preferably concentrically positioned with respect to the optical axis 128 of the detector 110, preferably defines the viewing direction 164 of the detector 110. A coordinate system 166 can be defined, in which the direction parallel or antiparallel to the optical axis 128 can be defined as the longitudinal direction, and the direction perpendicular to the optical axis 128 can be defined as the transverse direction. In Figure 1 the coordinate system 166 symbolically depicted, the longitudinal direction is represented by z, and the transverse directions are represented by x and y, respectively. Other types of coordinate systems are also feasible, such as non-Cartesian coordinate systems.

[0486] As outlined above, determining the position of the object 112 and / or a part thereof by using the detector 110 can be used to provide a human-machine interface 142 in order to provide at least one piece of information to the machine 168. In Figure 1In the embodiment schematically depicted, the machine 168 can be a computer and / or can include a computer. Other embodiments are possible. The evaluation device 132 can even be fully or partially integrated into the machine 168, such as integrated into a computer.

[0487] As outlined above, Figure 1 An example of a tracking system 146 is also depicted, which is configured to track the position of at least one object 112 and / or a part thereof. The tracking system 146 includes a detector 110 and at least one tracking controller 170. The tracking controller 170 can be adapted to track a series of positions of the object 112 at a particular point in time. The tracking controller 170 can be a stand-alone device and / or can be fully or partially integrated into the machine 168 (specifically, a computer, as Figure 1 shown) and / or integrated into the evaluation device 132.

[0488] Similarly, as outlined above, the human-machine interface 142 can form part of the entertainment device 144. The machine 168 (specifically, a computer) can also form part of the entertainment device 144. Thus, by using the user 160 as the object 112 and / or by the user 160 manipulating a control device that serves as the object 112, the user 160 can input at least one piece of information (such as at least one control command) into the computer, thereby changing the entertainment function, such as controlling the processes of the computer.

[0489] Figure 1 An example of an inertial measurement unit 172 used in an electronic device is also depicted. The inertial measurement unit 172 is adapted to receive data determined by the detector 110. The inertial measurement unit 172 is further adapted to receive data determined by at least one other sensor selected from the group consisting of: a wheel speed sensor, a steering rate sensor, an inclination sensor, an orientation sensor, a motion sensor, a magnetohydrodynamic sensor, a force sensor, an angle sensor, an angular rate sensor, a magnetic field sensor, a magnetometer, an accelerometer; a gyroscope, wherein the inertial measurement unit is adapted to determine at least one characteristic of the electronic device by evaluating data from the detector and the at least one other sensor, the characteristic being selected from the group consisting of: spatial position, relative or absolute motion in space, rotation, acceleration, orientation, angular position, inclination, steering rate, speed.

[0490] The inertial measurement unit 172 may include the detector 110 and / or may be connected to the detector 110 via at least one data connection. The evaluation device 132 of the inertial measurement unit 172 and / or at least one processing device may be configured to determine at least one combined distance information, in particular using at least one recursive filter. The recursive filter may be configured to determine the combined distance information taking into account other sensor data and / or other parameters (such as other sensor data from other sensors of the inertial measurement unit 172).

[0491] List of reference numerals

[0492] 110 Detector

[0493] 112 Object

[0494] 116 Sensor element

[0495] 118 Matrix

[0496] 120 Optical sensor

[0497] 122 Photosensitive area

[0498] 124 Irradiation source

[0499] 126 Irradiation beam

[0500] 128 Optical axis

[0501] 129 Transfer device

[0502] 130 Reflected beam

[0503] 132 Evaluation device

[0504] 134 Divider

[0505] 136 Camera

[0506] 138 Detector system

[0507] 140 Beacon device

[0508] 142 Human-machine interface

[0509] 144 Entertainment device

[0510] 146 Tracking system

[0511] 148 Scanning system

[0512] 150 Image analysis device

[0513] 152 Position evaluation device

[0514] 154 Connector

[0515] 156 Housing

[0516] 158 Control device

[0517] 160 User

[0518] 162 Opening

[0519] 164 Viewing direction

[0520] 166 Coordinate system

[0521] 168 Machine

[0522] 170 Tracking controller

[0523] 172 Inertial measurement unit

[0524] Cited references

[0525] US2016 / 0206216 A1

[0526] US2016 / 155006 A1

[0527] “Lasertechnik in der Medizin: Grundlagen, Systeme, Anwendungen”, “Wirkung von Laserstrahlung auf Gewebe”, 1991, pages 171 to 266, Jürgen Eichler, Theo Seiler, Springer Verlag, ISBN 0939 - 0979

[0528] WO 2014 / 097181 A1

[0529] WO 2018 / 091640 A1

[0530] WO 2018 / 091649 A1 and WO 2018 / 091638 A2

[0531] chapter 2 in X.Jiang, H.Bunke:,, Dreidimensionales Computersehen“ Springer, Berlin Heidelberg, 1997

[0532] R.A.Street(Ed.): Technology and Applications of Amorphous Silicon, Springer - Verlag Heidelberg, 2010, pp.346 - 349

[0533] WO 2012 / 110924 A1

[0534] DE 198 46 619A1

[0535] CN 108 363 482A

[0536] US2018 / 033146 A1

Claims

1. A detector (110) for identifying a material property m, comprising: - A sensor element (116) comprising a matrix (118) of optical sensors (120), each of the optical sensors (120) having a photosensitive area (122), wherein the sensor element (116) is configured to capture a reflected image of a light beam originating from an object (112); - An evaluation device (132) configured to determine the material property from the reflected image, Wherein, the evaluation device (132) is configured to determine at least one distance feature by applying at least one distance-dependent image filter Ф1 to the reflected image Wherein, the distance-dependent image filter is an image filter whose output is related to the distance between the object (112) and the detector (110) Wherein, the evaluation device (132) is configured to determine at least one material feature by applying at least one material-related image filter Ф2 to the reflected image Wherein, the material-related image filter Ф2 is a mathematical operation that maps the reflected image or a region of interest in the reflected image to the material feature and wherein, the material feature is related to the material of the object (112), and wherein, the evaluation device (132) is configured to determine the ordinate z by evaluating the distance feature and to determine the material property m by evaluating the material feature wherein, the evaluation device (132) is configured to determine the ordinate z by evaluating the distance feature 2. The detector (110) according to claim 1, wherein, The material property is determined from the intensity distribution of the light spots in the reflected image.

3. The detector (110) according to claim 1 or 2, wherein, More than one material feature is determined by applying more than one material-related image filter to the reflected image.

4. The detector (110) according to any one of claims 1 to 3, wherein, The material property is a property selected from the group comprising: scattering coefficient, translucency, transparency or deviation from Lambertian surface reflection.

5. The detector (110) according to any one of claims 1 to 4, wherein, The material-related image filter is at least one filter selected from the group comprising: a light spot shape filter; A contrast filter based on gray level occurrence; an energy filter based on gray level occurrence; a homogeneity filter based on gray level occurrence; a dissimilarity filter based on gray level occurrence; a Lowe's energy filter; a threshold region filter; or a linear combination thereof.

6. The detector (110) according to any one of claims 1 to 5, wherein, The material property m and / or the ordinate z are determined by using a predetermined relationship between z and m.

7. The detector (110) according to any one of claims 1 to 6, wherein The material property m and / or the ordinate z are determined by the function and / or respectively.

8. The detector (110) according to any one of claims 1 to 7, wherein, The sensor element (116) comprises a CMOS sensor.

9. The detector (110) according to any one of claims 1 to 8, wherein, The detector (110) comprises an illumination source (124), wherein the illumination source (124) is configured to generate an illumination pattern for illuminating the object, wherein the illumination pattern comprises a dot pattern.

10. The detector (110) according to claim 9, wherein, The illumination source emits light having a wavelength of 700 nm to 1000 nm.

11. The detector (110) according to claim 9 or 10, wherein, The illumination source comprises a vertical cavity surface emitting laser.

12. The detector (110) according to any one of claims 9 to 11, wherein, The illumination source is configured to generate a random dot pattern or a quasi-random pattern for illuminating the object.

13. The detector (110) according to any one of claims 9 to 12, wherein, The illumination source and the optical sensor (120) are arranged in a common plane.

14. The detector (110) according to any one of claims 1 to 13, wherein, The detector (110) is integrated into a smart phone.

15. The detector (110) according to any one of claims 1 to 14, wherein, The detector (110) is configured to determine and / or verify whether the surface to be inspected is human skin or includes human skin.

16. A use of a detector (110) according to any one of claims 1-15, for use purposes, the use being selected from the group comprising: position measurement in traffic technology; entertainment applications; security applications; surveillance applications; safety applications; human-machine interface applications; logistics applications; tracking applications; outdoor applications; mobile applications; communication applications; photographic applications; machine vision applications; robotic applications; quality control applications; manufacturing applications.

17. A method for determining the material property of an object (112), the method comprising the following steps: a) Capturing a reflected image of the object (112) by using a sensor element (116) having a matrix (118) of optical sensors (120), each of the optical sensors (120) having a photosensitive area (122); b) Determining the material property by evaluating the reflected image, including: b1) Determining at least one distance feature by applying at least one distance-related image filter Ф1 to the reflected image wherein the distance-related image filter is an image filter whose output is related to the distance between the object and the reflected image recording sensor b2) Determining at least one material feature by applying at least one material-related image filter Ф2 to the reflected image wherein the material-related image filter Ф2 maps the reflected image or a region of interest in the reflected image to the material feature is a mathematical operation, and wherein the material feature is related to the material of the object (112), and b3) By evaluating the distance feature to determine the ordinate z and by evaluating the material feature to determine the material property m.

18. The method according to claim 17, wherein, The material-related image filter is at least one filter selected from the group consisting of: a spot shape filter; a contrast filter based on gray level occurrence; an energy filter based on gray level occurrence; a homogeneity filter based on gray level occurrence; a dissimilarity filter based on gray level occurrence; a Lowe's energy filter; a threshold region filter; or a linear combination thereof.

19. The method according to claim 17 or 18, wherein, The object is irradiated with a dot pattern.

20. The method according to any one of claims 17 to 19, wherein The object is irradiated with light having a wavelength of 700 nm to 1000 nm.

21. The method according to any one of claims 17 to 20, wherein, The material property is determined from the intensity distribution of the spots in the reflected image.

22. The method according to any one of claims 17 to 21, wherein More than one material feature is determined by applying more than one material-related image filter to the reflected image.

23. The method according to any one of claims 17 to 22, wherein The method further includes image analysis and / or image processing to identify reflection features in the reflected image.

24. The method according to claim 23, wherein The image analysis and / or image processing includes selecting a region of interest.

25. The method according to any one of claims 17 to 24, wherein The method further includes determining and / or verifying whether the surface to be inspected is human skin or includes human skin.

Images