Method for measuring surface roughness

Abstract

A method for measuring the surface roughness of an object, wherein this method is: a) Receiving a speckle image showing an object while it is being irradiated by coherent electromagnetic radiation associated with wavelengths between 850 nm and 1400 nm, b) Determining the surface roughness measurement based on the speckle image, c) To provide the measurement value of the surface roughness, Includes.

Description

Technical Field

[0001] The present invention relates to a method for measuring surface roughness, a non - transient computer - readable storage medium, the use of surface roughness measurement, a device or system for measuring surface roughness.

Background Art

[0002] Current measurement of surface roughness uses light in the visible range. These setups require a fixed setup. In many cases, direct contact and an expert are required to perform the measurement.

[0003] Alternatively, conductivity - based measurements are developed that require contact with the surface being measured while providing inaccurate measurement results.

Summary of the Invention

Problems to be Solved by the Invention

[0004] Therefore, there is a desire to provide an easy, reliable, and non - contact method and device for measuring surface roughness.

Means for Solving the Problems

[0005] In one aspect, a method for measuring surface roughness associated with an object is disclosed. The method includes: a) receiving a speckle image representing an object irradiated with coherent electromagnetic radiation related to wavelengths between 850 nm and 1400 nm; b) determining a surface roughness measurement value based on the speckle image; and c) providing the surface roughness measurement value.

[0006] In one embodiment, a non-temporary computer-readable storage medium is disclosed, which, when executed by a computer, includes instructions to cause the computer to: a) receive a speckle image showing an object irradiated with coherent electromagnetic radiation relating to wavelengths between 850 nm and 1400 nm; b) determine a surface roughness measurement based on the speckle image; and c) provide the surface roughness measurement.

[0007] In one embodiment, the use of surface roughness measurements to evaluate human surface roughness is disclosed.

[0008] In one embodiment, a device or system for measuring surface roughness is disclosed, the system or device comprising: an illumination source configured to illuminate an object with coherent electromagnetic radiation relating to wavelengths between 850 nm and 1400 nm; a camera configured to generate a speckle image showing the object under illumination with coherent electromagnetic radiation relating to wavelengths between 850 nm and 1400 nm; and a processor that receives the speckle image from the camera, determines a surface roughness measurement based on the speckle image, and provides the surface roughness measurement.

[0009] The disclosures, embodiments, and examples described herein relate to the methods, systems, apparatus, chemical products, and computer elements listed above and below. Advantageously, the advantages provided by any embodiment and example apply equally to all other embodiments and examples.

[0010] Embodiment The following sections outline the terms and / or the technical fields of this disclosure used herein by definition and / or by example. Where examples are given, it should be understood that this disclosure is not limited to such examples.

[0011] Skin roughness constitutes an important parameter for assessing skin health. Currently, skin roughness is assessed inaccurately and / or under artificial conditions. Often, direct contact with the skin is required, which can disturb the person being assessed. This can cause discomfort and lead to an increase in their stress level. For example, an increased stress level can cause sweating, which in turn can interfere with the measurement of surface roughness. Furthermore, performing the measurement requires experts and advanced, costly technology. Therefore, there is a need for a low-cost, simple, reliable, and non-invasive method of measuring surface roughness.

[0012] By employing the methods, devices, and systems described herein, surface roughness can be easily measured with low-cost hardware. Such hardware is readily available and can be easily integrated into mobile electronic devices such as smartphones. Furthermore, it saves resources that would otherwise be required for time-consuming measurements. Thus, determining surface roughness measurements based on speckle images showing objects such as humans irradiated with coherent electromagnetic radiation associated with wavelengths between 850 nm and 1400 nm enables surface roughness measurement using mobile electronic devices. Since these devices can be operated even by users who are not experts in mobile electronic devices, measurements can be taken under more natural conditions. Furthermore, since the wavelengths of coherent electromagnetic radiation are invisible to the human eye, humans, especially the eyes, are not disturbed or interfered with by the measurement. In addition, measurements can be performed in darkness. Another advantage is that surface roughness measurements can be performed under ambient light. The wavelengths of coherent electromagnetic radiation are selected so as to eliminate and / or ignore the contribution of ambient light to the intensity signal associated with the speckle image. Since surface roughness is measured based on speckle images, contact with human skin is not required, allowing for non-contact operation while still enabling high-speed speckle image analysis to determine surface roughness measurements.

[0013] In one embodiment, the camera may refer to a device having at least one image sensor configured to generate or record spatially resolved one-dimensional, two-dimensional, or even three-dimensional optical data or information, without limitation. The camera may be a digital camera. As an example, the camera may include at least one image sensor configured to record an image, for example, at least one CCD sensor and / or at least one CMOS sensor.

[0014] In one embodiment, the camera may include an image sensor and a lens. Preferably, the camera may include an image sensor, a lens, and a polarizer. The lens may refer to an optical element suitable for influencing the expansion of a light beam associated with coherent electromagnetic radiation. The polarizer may refer to an optical element suitable for selecting electromagnetic radiation according to its polarization. In particular, the polarizer may refer to an optical element suitable for selecting coherent electromagnetic radiation according to its polarization. Thus, some of the electromagnetic radiation, especially coherent electromagnetic radiation, may pass through the polarizer, while the remaining electromagnetic radiation, especially coherent electromagnetic radiation, may be at least partially deflected and / or at least partially absorbed.

[0015] Coherent electromagnetic radiation associated with wavelengths between 850 nm and 1400 nm penetrates deep into the skin, and some of the information obtained from the light reflected from the skin includes information independent of surface roughness, which interferes with surface roughness measurements. Polarizers can be used to improve the signal-to-noise ratio. Coherent electromagnetic radiation reflected from the surface of an object typically has a different polarization than the light reflected from the deeper layers of human skin. Therefore, polarizers allow for the selection of desired signals from unwanted signals.

[0016] In one embodiment, coherent electromagnetic radiation may refer to electromagnetic radiation that can exhibit interference effects. It may also include partial coherence, i.e., imperfect correlation between phase values. Preferably, coherent electromagnetic radiation may be in the infrared range. Coherent electromagnetic radiation may be associated with wavelengths between 850 nm and 1400 nm. Preferably, coherent electromagnetic radiation may be associated with wavelengths between 880 nm and 1300 nm. In one embodiment, coherent electromagnetic radiation may be associated with a wavelength range between 900 nm and 1000 nm, and / or coherent electromagnetic radiation may be associated with a wavelength range between 1100 nm and 1200 nm, and / or coherent electromagnetic radiation may be associated with a wavelength range between 1340 nm and 1440 nm. This is advantageous because sunlight has a band gap in these regions. Therefore, coherent electromagnetic radiation having the aforementioned wavelengths irradiated to produce a speckle image can be easily distinguished from incident sunlight. Therefore, the coherent electromagnetic radiation used within the specified region enables surface roughness measurement even in the presence of sunlight, similar to natural environments. This allows surface roughness measurement to be performed easily and independently of location. Overall, the signal-to-noise ratio is improved, and the accuracy of surface roughness evaluation is enhanced.

[0017] In one embodiment, the computer-readable storage medium may refer to any suitable data storage device or computer-readable memory storing one or more instruction sets (e.g., software) that embody any one or more of the methodologies or functions described herein. These instructions may reside in main memory and / or in the running processor, main memory, and processing devices of the computer constituting the computer-readable storage medium. The instructions may further be transmitted or received across a network via a network interface device. The computer-readable storage medium includes, for example, a hard drive on a server, a USB storage device, a CD, a DVD, or a Blu-ray disc.

[0018] In one embodiment, a speckle pattern may refer to a distribution of multiple specks. A distribution of multiple specks may refer to the spatial distribution of at least one of the multiple specks, and / or the spatial distribution of the relationships between at least two of the multiple specks. At least one spatial distribution of the multiple specks may refer to and / or specify the spatial extent of at least one of the multiple specks. At least two spatial distributions of the multiple specks may refer to and / or specify the spatial extent of a first speckle among the at least two specks relative to a second speckle among the at least two specks, and / or the distance between the first speckle and the second speckle among the at least two specks.

[0019] In one embodiment, the illumination source may refer to a device suitable for irradiating an object with coherent electromagnetic radiation and / or emitting coherent electromagnetic radiation. The illumination source may include at least one radiation source. The illumination source may include a plurality of radiation sources. The illumination source may include, for example, at least one laser light source and / or at least one semiconductor radiation source. The semiconductor radiation source may be a light-emitting diode such as an organic light-emitting diode, a laser diode, and / or an inorganic light-emitting diode. Additionally or alternatively, the radiation source may be a VCSEL array and / or LEDs. Additionally or alternatively, the illumination source may include a VCSEL array and / or LEDs. Furthermore, the illumination source may include one or more optical elements. The optical elements may be, for example, lenses, metasurface elements, DOEs, or a combination thereof. Thus, the illumination source may include one or more radiation sources and one or more optical elements.

[0020] In one embodiment, the camera may refer to at least one unit of an optoelectronic device configured to produce at least one image. The image may be produced via hardware and / or a software interface considered to be the camera. The camera may include at least one image sensor, in particular at least one pixelated image sensor. The camera may include at least one CMOS sensor and / or at least one CCD chip. For example, the camera may include at least one CMOS sensor that may be sensitive to the infrared spectral range. The camera may have a field of view between 10°×10° and 75°×75°, preferably 55°×65°. The camera may have a resolution of less than 2 megapixels, preferably between 0.3 megapixels and 1.5 megapixels. Megapixel may refer to a unit for measuring the number of pixels related to the camera and / or image. The camera may include further elements such as one or more optical elements (e.g., one or more lenses). As an example, the optical sensor may be a fixed-focus camera having at least one lens fixedly tuned to the camera. However, alternatively, the camera may also include one or more variable lenses that can be adjusted automatically or manually. However, other cameras are also capable of this.

[0021] In one embodiment, the model may be suitable for determining an output based on an input. In particular, the model may be suitable for determining a surface roughness measurement based on a speckle image, preferably based on an received speckle image. The model may be a physical model, a data-driven model, or a hybrid model.

[0022] A hybrid model may be a model that includes at least one data-driven model with physical or statistical adaptations and model parameters. Statistical or physical adaptations may be introduced to improve the quality of results, as they provide a systematic relationship between empirical rules and theory.

[0023] In one embodiment, a data-driven model can represent the correlation between surface roughness measurements and speckle images. The data-driven model can obtain the correlation between surface roughness measurements and speckle images based on a training dataset that includes multiple speckle images and multiple surface roughness measurements.

[0024] In one embodiment, a data-driven model may be parameterized based on a training dataset to receive speckle images and provide surface roughness measurements. The data-driven model may be trained based on a training dataset. The training dataset may include at least one speckle image and at least one corresponding surface roughness measurement. The training dataset may include multiple speckle images and multiple surface roughness measurements. Training the model may include parameterizing the model. The data-driven model may be parameterized and / or trained to provide surface roughness measurements based on speckle images, particularly received speckle images. Determining surface roughness measurements based on speckle images may include providing speckle images to the data-driven model and receiving surface roughness measurements from the data-driven model. Providing surface roughness measurements based on speckle images may include mapping speckle images to surface roughness measurements. The data-driven model may be parameterized and / or trained to receive speckle images. The data-driven model may receive speckle images in its input layer.

[0025] The term "training" may also be denoted as "learning". This term may specifically refer, without limitation, to the process of constructing a data-driven model, particularly to determining and / or updating the parameters of a data-driven model. The parameter update of a data-driven model may also be called retraining. When referring to training in this specification, relearning may be included. During training, a data-driven model can be adjusted to achieve an optimal fit with the training data, for example, by associating at least one input value with at least one desired output value in an optimal fit. For example, if the neural network is a feed-forward neural network such as a CNN, the backpropagation algorithm may be applied to the training of the neural network. In the case of an RNN, for training purposes, the gradient descent algorithm or the time backpropagation algorithm may be employed. The training of a data-driven model may, without limitation, include or refer to the calibration of the model.

[0026] In one embodiment, the physical model can reflect physical phenomena in a mathematical form, including, for example, a first-principles model. The physical model may include a set of equations that describe the interaction between an object and coherent electromagnetic radiation, thereby obtaining surface roughness measurement values. The physical model can be based on at least one of a fractal dimension, a speckle size, a speckle contrast, a speckle modulation, a roughness exponent, a standard deviation of the height associated with surface features, a lateral correlation length, an average mean height, a root mean square height, or a combination thereof. In particular, the physical model can include one or more equations that relate the speckle image and the surface roughness measurement values based on an equation related to a fractal dimension, a speckle size, a speckle contrast, a speckle modulation, a roughness exponent, a standard deviation of the height associated with surface features, a lateral correlation length, an average mean height, a root mean square height, or a combination thereof.

[0027] In one embodiment, the object can refer to a physical entity. The object may include one or more materials. The materials may include skin (especially human skin), paper, wood, glass, metal oxides such as aluminum oxide, leather, and the like. In particular, the object can refer to a living body. Preferably, the object can be a human. When the object is a living body such as a human, at least a part of the skin of the living body such as a human can be irradiated with coherent electromagnetic radiation.

[0028] In one embodiment, the object can be a human, and the surface roughness can be related to human skin. For this purpose, the speckle image can show at least a part of human skin while being irradiated with coherent electromagnetic radiation.

[0029] In one embodiment, patterned coherent electromagnetic radiation may refer to multiple light beams of coherent electromagnetic radiation. Patterned coherent electromagnetic radiation may include multiple light beams, for example, at least two light beams, preferably at least two light beams. The projection of the light beams of patterned coherent electromagnetic radiation onto a surface may produce light spots. Therefore, the projection of multiple light beams of patterned coherent electromagnetic radiation onto an object may produce multiple light spots on the object. Preferably, the number of light spots may be equal to the number of light beams associated with the patterned coherent electromagnetic radiation. One or more light spots may be shown in the speckle image. The projection of patterned coherent electromagnetic radiation onto a regular surface may produce light spots projected onto the regular surface independently of speckles. The projection of patterned coherent electromagnetic radiation onto a regular surface may produce light spots projected onto an irregular surface containing at least one speckle, preferably multiple speckles. The object may have at least partially irregular surfaces. Therefore, the speckle image may contain multiple speckles. In particular, if the object includes skin at least partially, the interference of coherent electromagnetic radiation will form multiple speckles. Therefore, a light spot may contain zero, one, or more speckles, depending on the surface onto which the patterned coherent electromagnetic radiation is projected. Skin may have an irregular surface. For this reason, the projection of patterned coherent electromagnetic radiation may result in the formation of speckles within one or more light spots. A light spot may be the result of the projection of a light beam associated with patterned coherent electromagnetic radiation. A light spot refers to a spot of any shape of coherent electromagnetic radiation. A light spot may refer to a continuous area illuminated by coherent electromagnetic radiation. The projection of coherent electromagnetic radiation onto an irregular surface results in the formation of speckles. Therefore, a light spot may contain one or more speckles. A light spot may have a diameter between 0.5 mm and 5 cm, preferably between 0.6 mm and 4 cm, more preferably between 0.7 mm and 3 cm, and most preferably between 0.4 mm and 2 cm.

[0030] For example, patterned coherent electromagnetic radiation can be generated by an illumination source including multiple optical emitters (e.g., a VCSEL array containing multiple VCSELs). One emitter of the multiple optical emitters may emit one light beam. Thus, one emitter of the multiple optical emitters may be associated with one light spot, the formation of one light spot, and / or the projection of one light spot. Additionally or alternatively, patterned coherent electromagnetic radiation can be generated by one or more optical emitters and optical elements such as DOEs or metasurface elements. The metasurface element may be a metalens. The metalens may be at least partially transparent to coherent electromagnetic radiation and / or may contain materials associated with nanoscale structures. The optical element may replicate the number of light beams associated with one or more optical emitters and / or may be suitable for replicating the number of light beams associated with one or more optical emitters. For example, the optical emitters may be lasers.

[0031] Additionally or alternatively, patterned coherent electromagnetic radiation can be generated by one or more optical elements, such as optical emitters and DOEs or metasurface elements. The optical elements may replicate the number of light beams associated with one or more optical emitters, and / or may be suitable for replicating the number of light beams associated with one or more optical emitters. For example, the optical emitters may be lasers.

[0032] In one embodiment, a processor may refer to any logic circuit and / or generally a device configured to perform basic operations of a computer or system. In particular, a processor or computer processor may be configured to process basic instructions that drive a computer or system. It may be a semiconductor-based processor, a quantum processor, or other type of processor configured to process instructions. For example, a processor may be or comprise a central processing unit ("CPU"). A processor may be a graphics processing unit ("GPU"), a tensor processing unit ("TPU"), a complex instruction set computing ("CISC") microprocessor, a reduced instruction set computing ("RISC") microprocessor, a very long instruction word ("VLIW") microprocessor, or a processor implementing a processor or combination of instruction sets. Processing means may be one or more application-specific processing devices, such as application-specific integrated circuits ("ASIC"), field-programmable gate arrays ("FPGA"), composite programmable logic devices ("CPLD"), digital signal processors ("DSP"), or network processors. The methods, systems, and devices described herein may be implemented as software within a DSP, microcontroller, or other side processor, or as hardware circuitry within an ASIC, CPLD, or FPGA. The term "processor" may also refer to one or more processing devices, such as a distributed system of processing devices deployed across multiple computer systems (e.g., cloud computing), and should be understood to be not limited to a single device unless otherwise specified. A processor may also be an interface to a remote computer system, such as a cloud service. A processor may include, or may be, a secure enclave processor (SEP). An SEP may be a secure circuit configured for spectral processing. "Secure circuitry" means circuitry that protects internal resources isolated from direct access by external circuitry.The processor may be an image signal processor (ISP) and may include circuitry suitable for processing images, particularly images containing personal and / or confidential information.

[0033] In one embodiment, speckle may refer to an optical phenomenon caused by the interference of coherent electromagnetic radiation by irregular or irregular surfaces. Speckle may appear as contrast fluctuations in images, such as speckle images.

[0034] In one embodiment, a speckle image may refer to an image showing multiple speckles. A speckle image can show multiple speckles. A speckle image can be generated while an object is irradiated with coherent electromagnetic radiation associated with wavelengths between 850 nm and 1400 nm. A speckle image may show a speckle pattern. A speckle pattern can identify the distribution of speckles. A speckle image can show the spatial extent of speckles. A speckle image may be suitable for determining surface roughness measurements. A speckle image may be generated using a camera. To generate a speckle image, an object may be irradiated with an illumination source.

[0035] In one embodiment, a surface feature may refer to a structure of any shape relating to the surface of an object. In particular, a surface feature may refer to a substructure of the surface relating to the object. The surface may include multiple surface features. For example, a ridge or a depression may be a surface feature. Preferably, a surface feature may refer to a part of the surface relating to an angle other than 90° with respect to the surface normal.

[0036] In one embodiment, surface roughness can refer to a surface characteristic related to an object. In particular, surface roughness can characterize the lateral and / or vertical extent of surface features. Surface roughness can be evaluated based on surface roughness measurements. Surface roughness measurements can quantify surface roughness. For example, a surface.

[0037] In one embodiment, a surface roughness measurement may refer to a measurement suitable for quantifying surface roughness. A surface roughness measurement may be related to the speckle pattern. For example, a surface roughness measurement may include at least one of the following: fractal dimension, speckle size, speckle contrast, speckle modulation, roughness index, standard deviation of height related to surface features, lateral correlation length, mean mean height, root mean square height, or a combination thereof. Preferably, a surface roughness measurement may be suitable for describing vertical and lateral surface features. A surface roughness measurement may include values ​​related to the surface roughness measurement. A surface roughness measurement may refer to terms for the quantity measuring surface roughness and / or values ​​related to the quantity measuring surface roughness.

[0038] In one embodiment, the fractal dimension may be determined based on the Fourier transform of the speckle image and / or the inverse Fourier transform of the speckle image. In particular, the fractal dimension can be determined based on the slope of a linear function fitted to a double-log plot of power spectral density versus frequency obtained by the Fourier transform. Speckle size may refer to the spatial extent of one or more speckles. If the speckle size refers to the spatial extent of multiple speckles, the speckle size can be determined based on the average of the multiple speckle sizes and / or the weighting of the multiple speckle sizes. Speckle contrast may refer to a measure of the standard deviation of at least a portion of the speckle image relative to the average brightness of at least a portion of the speckle image. Speckle modulation may refer to a measure of the intensity variation associated with speckles in at least a portion of the speckle image. Roughness index, standard deviation of height associated with surface features, transverse correlation length, or a combination thereof can be determined based on an autocorrelation function associated with a double-log plot of power spectral density versus frequency obtained by the Fourier transform.

[0039] In one embodiment, determining a surface roughness measurement based on a speckle image may mean determining a surface roughness measurement based on a speckle pattern. Determining surface roughness based on a speckle pattern may mean determining surface roughness based on the distribution of multiple specks in a speckle image. Determining a surface roughness measurement based on the distribution of multiple specks in a speckle image may mean determining the distribution of multiple specks in a speckle image. Determining the distribution of specks may include determining at least one of the following: the fractal dimension associated with the speckle image, the speckle size associated with the speckle image, the speckle contrast associated with the speckle image, the speckle modulation associated with the speckle image, the roughness index associated with the speckle image, the standard deviation of height associated with the surface features associated with the speckle image, the lateral correlation length associated with the speckle image, the average mean height associated with the speckle image, the root mean square height associated with the speckle image, or a combination thereof. Additionally or alternatively, determining surface roughness measurements may include determining at least one of the following: fractal dimension associated with the speckle image, speckle size associated with the speckle image, speckle contrast associated with the speckle image, speckle modulation associated with the speckle image, roughness index associated with the speckle image, standard deviation of height associated with surface features associated with the speckle image, lateral correlation length associated with the speckle image, average mean height associated with the speckle image, root mean square height associated with the speckle image, or a combination thereof.

[0040] Since speckles are caused by surface irregularities, they reflect surface roughness. Therefore, determining surface roughness measurements based on speckles in a speckle image utilizes the relationship between speckle distribution and surface roughness. This enables a low-cost, efficient, and readily available solution for surface roughness assessment, separate from fixed medical or cosmetic contexts.

[0041] In one embodiment, coherent electromagnetic radiation may be patterned coherent electromagnetic radiation, and / or coherent electromagnetic radiation may include one or more light beams. Preferably, coherent electromagnetic radiation may include at least two, more preferably at least five light beams. Patterned coherent electromagnetic radiation may refer to coherent electromagnetic radiation associated with multiple light beams, and / or coherent electromagnetic radiation including multiple light beams. One light beam may illuminate at least a portion of an object, and / or may be associated with a continuous area of ​​coherent electromagnetic radiation in at least a portion of the object. A light spot may refer to a continuous area of ​​coherent electromagnetic radiation in at least a portion of an object. Thus, an object illuminated by patterned coherent electromagnetic radiation may be illuminated by multiple light spots. The light spots may overlap at least partially. The intensities associated with the light spots may be substantially similar. Substantially similar means that the intensity values ​​associated with the light spots may differ by less than 50%, preferably less than 30%, and more preferably less than 20%. Using patterned lighting is advantageous because it can protect light-sensitive areas such as the eyes.

[0042] In one embodiment, the determination of a surface roughness measurement can be based on the distribution of speckles in a speckle image. Determining a surface roughness measurement based on the distribution of speckles in a speckle image may include determining at least one of the following: the size distribution of speckles, the power spectral density associated with the speckle image, the fractal dimension associated with the speckle image, the speckle contrast, the speckle modulation, or a combination thereof. Additionally or alternatively, determining a surface roughness measurement based on the distribution of speckles in a speckle image may include providing the speckle image to a model, in particular a data-driven model, which can be parameterized and / or trained on a training dataset containing one or more speckle images and one or more corresponding surface roughness measurements.

[0043] In one embodiment, the distance between the object and the camera used to generate the speckle image may be between 10 cm and 1.5 m, and / or the distance between the object and the illumination source used to illuminate the object may be between 10 cm and 1.5 m. Preferably, the distance between the object and the camera may be between 20 cm and 1.2 m. Preferably, the distance between the object and the illumination source may be between 20 cm and 1.2 m. By adjusting the distance between the object and the camera, it is ensured that a speckle image of sufficient quality is generated. Thus, the specified distances enable accurate and reliable determination of surface roughness. This is particularly important in non-static situations where the user may interact with the device themselves.

[0044] In one embodiment, a speckle image can show a person irradiated by coherent electromagnetic radiation, and the surface roughness of the person can be determined. Preferably, the person can generate and / or initiate the generation of the speckle image. Preferably, the speckle image can be initiated by a person operating an application on a mobile electronic device. This allows the person to decide when to determine the surface roughness of the skin. Thus, it becomes possible for non-expert users to determine the surface roughness, and measurements can be made under more natural and unartificial conditions. This allows for a more realistic assessment of surface roughness, which in turn leads to more realistic measurements of surface roughness. For example, skin may have surface roughness that changes throughout the day depending on human activity. Playing sports, as well as applying cream to the skin, can affect surface roughness. This effect can be verified by the methods and systems described herein.

[0045] In one embodiment, surface roughness measurements may be determined by a mobile electronic device, and / or speckle images may be generated by the camera of the mobile electronic device. In particular, a person can initiate the generation of speckle images based on the mobile electronic device. This is beneficial because many people own mobile electronic devices such as smartphones. Since these devices are carried by people, surface roughness measurements can be performed at any time and in a more natural and unartificial setting. This allows surface roughness to be assessed more realistically, which in turn leads to more realistic measurements of surface roughness.

[0046] In one embodiment, the surface roughness measurement may be determined based on the speckle image by providing the speckle image to the model and receiving the surface roughness measurement from the model. The model is a data-driven model and may be parameterized and / or trained based on a training dataset containing multiple speckle images and corresponding surface roughness measurements or indices of surface roughness measurements. Additionally or alternatively, the model may be a physical model.

[0047] In one embodiment, the speckle image may be associated with a resolution of less than 5 megapixels. Preferably, the speckle image may be associated with a resolution of less than 3 megapixels, more preferably less than 2.5 megapixels, and most preferably less than 2 megapixels. Such speckle images can be generated with readily available, small, and inexpensive smartphone cameras. Furthermore, the memory capacity and processing power required to evaluate surface roughness measurements are small. Therefore, the low resolution of speckle images used for evaluating surface roughness enables their use for evaluating the surface roughness of mobile electronic devices, particularly smartphones or wearable devices (as these devices have severe limitations in size, memory, and processing power).

[0048] In one embodiment, the image augmentation technique may include at least one of the following: scaling, cutting, rotating, blurring, distorting, shearing, resizing, collapsing, changing contrast, changing brightness, adding noise, multiplying at least some of the pixel values, dropout, color adjustment, applying convolution, embossing, sharpening, inverting, averaging pixel values, etc.

[0049] In one embodiment, the method further includes reducing the speckle image to a predetermined size before determining the surface roughness measurement, and / or the processor may be further configured to reduce the speckle image to a predetermined size before determining the surface roughness measurement. Reducing the speckle image to a predetermined size can be based on applying one or more image augmentation techniques. Reducing the speckle image to a predetermined size may include selecting a speckle image area of ​​a predetermined size and cutting the speckle image to an area of ​​the predetermined size. The speckle image area of ​​the predetermined size may relate to living organisms such as humans, particularly the skin of living organisms such as human skin. The portion of the image other than the speckle image area of ​​the predetermined size may relate to the background and / or be independent of living organisms such as humans. This reduces the amount of data that needs to be processed, shortens the time required to determine the surface roughness, or reduces the required storage and processor. Furthermore, the portion of the image useful for analysis is selected. This allows portions of the speckle image that are unrelated to the object or living organisms such as humans to be ignored when the size is reduced. Thus, the surface roughness measurement can be easily determined and unnecessary portions unrelated to the object can be ignored in the analysis.

[0050] In one embodiment, the method may further include reducing the speckle image to a predetermined size based on detecting objects in the speckle image, and / or the processor may be further configured to reduce the speckle image to a predetermined size based on detecting objects in the speckle image. Preferably, the method may further include reducing the speckle image to a predetermined size based on detecting a user in the speckle image, and / or the processor may be further configured to reduce the speckle image to a predetermined size based on detecting a user in the speckle image. Preferably, the speckle image may be reduced to a predetermined size based on detecting objects in the speckle image before determining the surface roughness measurement. In particular, the speckle image may be reduced to a predetermined size based on detecting a user in the speckle image before determining the surface roughness measurement. Reducing the speckle image to a predetermined size based on detecting objects in the speckle image may include detecting the contour of an object and reducing the speckle image to an area related to the object. In particular, reducing a speckle image to a predetermined size based on detecting a user within the speckle image may include detecting the user's contour (e.g., detecting the contour of the user's face) and reducing the speckle image to an area related to the user (particularly an area related to the user's face). Preferably, the area related to the user may be within the contour of an object, particularly the contour of the user and / or the contour of the user's face. For this purpose, the method may further include receiving a flood image, and / or the processor may be further configured to receive a flood image. The flood image may be generated while an object is illuminated by flood illumination. The flood image may show the contour of an object, particularly the contour of a user. The contour of an object, particularly the contour of a user, may be detected based on the flood image.Preferably, the contours of objects, particularly the contours of a user, can be detected by providing a flood image to an object detection data-driven model, particularly a user detection model, which can be parameterized and / or trained to receive the flood image and provide representations of the object contours based on a training dataset containing the flood image and representations of the object contours. Representations of the object contours may include a plurality of points indicating the location of specific landmarks associated with the object. For example, if the speckle image is related to a user's face, the user's face can be detected based on the contours, which may indicate facial landmarks such as the tip of the nose, the outer corners of the lips, or eyebrows.

[0051] In one embodiment, the method may further include generating a partial speckle image, and / or the processor may be further configured to generate a partial speckle image. A partial speckle image may refer to a partial image generated based on a speckle image. A partial speckle image may be generated by applying one or more image augmentation techniques to the speckle image.

[0052] In one embodiment, the method may further include generating a first speckle image and a second speckle image, and / or the processor may be configured to generate the first speckle image and the second speckle image. The speckle image may include a first speckle image and a second speckle image. The first speckle image may refer to a first portion of the speckle image. The second speckle image may refer to a second portion of the speckle image. Preferably, the first speckle image and the second speckle image may be different from each other. In particular, the first speckle image and the second speckle image may not overlap. The first speckle image and the second speckle image can be generated by applying one or more image augmentation techniques to the speckle image. Determining a surface roughness measurement based on the speckle image may include determining a first surface roughness measurement based on the first speckle image and determining a second surface roughness measurement based on the second speckle image. Providing a surface roughness measurement may include providing a first surface roughness measurement and a second surface roughness measurement. In particular, the first surface roughness measurement and the second surface roughness measurement may be provided together. Preferably, the first and second surface roughness measurements can be provided in a surface roughness measurement map showing the spatial distribution of the surface roughness measurements. For example, the surface roughness measurement map may show the first surface roughness measurements associated with a first area within the surface roughness measurement map, and the second surface roughness measurement map may show the second surface roughness measurements associated with a second area within the surface roughness measurement map. In particular, the surface roughness measurement map can be analogous to a thermal map, and the surface roughness measurements can be plotted against the area associated with each surface roughness measurement.

[0053] In one embodiment, surface roughness measurements can be used to evaluate the surface roughness of human skin.

[0054] These and other objectives will become clear from reading the following description and are achieved by the subject matter of the independent claims. Dependent claims refer to embodiments of the present invention. [Brief explanation of the drawing]

[0055] The present disclosure will be further described below with reference to the attached drawings. The same reference numerals in the drawings and in this disclosure are intended to refer to the same or similar elements, components, and / or parts. [Figure 1] This figure shows an exemplary system for measuring surface roughness. [Figure 2] This figure shows an example of how to determine surface roughness measurements. [Figure 3] This figure shows an exemplary embodiment of a method for measuring the surface roughness of an object 300. [Figure 4] This figure shows a surface embodiment related to an object. [Modes for carrying out the invention]

[0056] Detailed explanation The following embodiments are merely examples, and not limiting, of how to implement the methods, systems, or application devices disclosed herein.

[0057] Figure 1 shows an exemplary system for measuring surface roughness. This system comprises irradiation sources 104, 106 and a processor 108. Surface roughness can be determined with respect to an object such as a human being. Preferably, the object may be a living organism 114 such as a human being. The living organism 114 may have skin. Skin may be associated with surface roughness. Surface roughness can be evaluated based on surface roughness measurements. Skin may be exposed to coherent electromagnetic radiation associated with wavelengths between 850 nm and 1400 nm.

[0058] Coherent electromagnetic radiation associated with wavelengths between 850 nm and 1400 nm can be emitted by the illumination source 104. The illumination source 104 may include one or more radiation sources, such as a VCSEL array or a single laser diode. The radiation source may be associated with one or more light beams. For example, a single laser diode can emit one light beam, while a VCSEL array can emit multiple light beams. Preferably, the number of light beams corresponds to the number of VCSELs in the VCSEL array. Thus, the illumination source may emit multiple light beams. Multiple light beams can project a pattern onto an object. Preferably, the illumination source 104 can emit patterned coherent electromagnetic radiation. Patterned coherent electromagnetic radiation may be suitable for projecting a pattern onto an object. Additionally or alternatively, the illumination source 104 may include one or more optical elements. The optical elements may be suitable for splitting and / or multiplexing the light beams. Examples of optical elements include diffractive optical elements, refractive optical elements, metasurface elements, lenses, and the like. Therefore, an illumination source 104, including a single laser diode or VCSEL array combined with an optical element, may result in irradiating an object with patterned coherent electromagnetic radiation. The illumination source 104 may be associated with an illumination field indicated by two lines emanating from the illumination source 104.

[0059] Depending on the body part irradiated by coherent electromagnetic radiation, different skin surface roughness measurements may be determined. Different body parts of the organism 114 may be associated with different skin roughness 116. For example, the hands may be associated with higher skin roughness, while the face may be associated with lower skin surface roughness. Surface roughness may be characteristic of the body part of the organism 114 and / or the identity of the organism 114.

[0060] Speckle images can be generated while a biological organism 114 is irradiated with coherent electromagnetic radiation, preferably patterned coherent electromagnetic radiation. When the coherent electromagnetic radiation is projected onto the skin of the biological organism 114, it can interact with the skin. Coherent electromagnetic radiation forms speckles when it interacts with an uneven and irregular surface such as skin. Different wavefronts of coherent electromagnetic radiation can interact through interference. Interference of different wavefronts of coherent electromagnetic radiation can result in contrast variations of the coherent electromagnetic radiation on the skin of the biological organism 114. These contrast variations may depend on the surface roughness associated with the surface irradiated by the coherent electromagnetic radiation. Therefore, roughness associated with the skin can affect the formation of speckles, including their size and orientation. Thus, surface roughness measurements can be obtained by analyzing the speckles.

[0061] To analyze the speckles, a speckle image is generated using a camera or similar device 106. 106 may include a sensor 110. Optionally, 106 may include a lens 112. In another example, 106 may include a polarizer. Coherent electromagnetic radiation lies in the infrared range. Therefore, coherent electromagnetic radiation can penetrate deeper than the epidermis. Surface roughness measurements can identify surface roughness related to the skin surface. Thus, information obtained from coherent electromagnetic radiation that has penetrated, for example, the dermis or deeper layers, can be superimposed on desired information related to the skin surface. A polarizer may be suitable for selecting coherent electromagnetic radiation reflected from the skin surface and for deselecting some of the coherent electromagnetic radiation that has interacted with skin layers, such as the dermis or deeper layers.

[0062] 106 may relate to a field of view indicated by two lines emanating from 106. 106 may have a field of view between 10°×10° and 75°×75°, preferably 55°×65°. 106 may have a resolution of less than 2 megapixels, preferably between 0.3 megapixels and 1.5 megapixels. An example of a speckle image may be shown in Figure 2.

[0063] The irradiation field may correspond to the field of view at least partially. At least a portion of the field of view related to 106 may be independent of the irradiation by coherent electromagnetic radiation. Therefore, the speckle image may show at least a portion of the object under irradiation by coherent electromagnetic radiation.

[0064] Furthermore, the speckle image may be provided to and / or received by processor 108. Processor 108 may include one or more processors. Processor 108 may determine surface roughness measurements based on the speckle image. Processor 108 may determine surface roughness measurements as described in the context of Figure 2.

[0065] All system components may be part of a device, such as a device for measuring surface roughness. In other embodiments, system components may be separated among multiple devices. For example, the processor 108 may be a server, while the light sources 104 and 106 may be part of a single device, such as a mobile electronic device. 106 can provide the processor 108 with a speckle image. The processor 108 can provide the surface roughness measurements to a device for displaying the surface roughness measurements and / or to a device for processing the surface roughness measurements. As an example, a device including 106 and the light source 104 may further include a display for displaying the surface roughness measurements and / or a surface roughness processor configured to further process the surface roughness measurements. Optionally, the device may include the processor 108.

[0066] Figure 2 shows an example of determining a surface roughness measurement. The surface roughness measurement is determined based on speckle images 202a and 202b. Examples of speckle images 202a and 202b are shown in Figure 2. Speckle image 204 may be cropped to a predetermined size. Speckle images 202a and 202b may be transformed by Fourier transform. The results of the Fourier transform of speckle images 202a and 202b may be called Fourier plots 204a and 204b. In particular, Fourier plots may be obtained by Fast Fourier Transform (FFT). Fourier plots 204a and 204b can represent the speckle images 202a and 202b in the frequency domain. Fourier plots 204a and 204b can represent the frequency distribution associated with speckle images 202a and 202b. Therefore, Fourier plots 204a and 204b can include the magnitude of the frequencies associated with the speckle images 202a and 202b. Fourier plots 204a and 204b can further be converted into power spectral density (PSD) plots 208. Fourier plots 204a and 204b can be converted into PSD plots 206a and 206b by multiplying the magnitude of each Fourier plot 204a and 204b by its conjugate. By radial averaging with respect to a given point, such as the center point of the quadratic image, a double-log magnitude-versus-frequency plot can be obtained, as shown on the right side of Figure 2. Radial averaging can refer to averaging values ​​at the same distance from a given point. Determining surface roughness based on PSD is advantageous because PSD can take into account vertical and transverse features. This provides a detailed image of the surface roughness related to the surface structure. Thus, a realistic description of the surface of an object becomes possible. Furthermore, it becomes possible to evaluate the distribution of surface irregularities.

[0067] A bilog-log plot can be used to visualize fractal dimension. Fractal dimension can be an example of a surface roughness measurement. Fractal dimension can be determined by fitting a linear function to the bilog-log magnitude-versus-frequency plots associated with speckle images 202a and 202b. In particular, fractal dimension can be determined as the slope of the linear function fitted to the bilog-log magnitude-versus-frequency plots associated with speckle images 202a and 202b. Higher surface roughness can correspond to a higher fractal dimension. Lower surface roughness can correspond to a lower fractal dimension.

[0068] Additionally or alternatively, surface roughness measurements may include one or more parameters of the autocorrelation function associated with speckle images 202a, 202b. The autocorrelation function can be obtained by the inverse Fourier transform of PSD plot 208. The autocorrelation function is defined as follows:

number

[0069] The parameters τ, σ, and ξ may be further examples of surface roughness measurements. ξ may be called the transverse correlation length. σ may refer to the standard deviation of height related to the surface features of the object. α may be called the roughness index. A high ξ may reflect low surface roughness, a high α may reflect high surface roughness, and a high σ may reflect high surface roughness. Further examples of surface roughness measurements include speckle contrast, speckle modulation, and speckle size. These examples are readily available from speckle images 202a and 202b.

[0070] Speckle Contrast K ij Preferably, this is the intensity value σ related to a given area of ​​the speckle images 202a, 202b. ij The standard deviation and the respective intensity values

number

number

[0071] Speckle modulation M can be calculated based on the following formula:

number

number

number

[0072] The speckle size can be calculated, for example, by multiplying the number of pixels by the pixel size. In some embodiments, the speckle size can be averaged over a portion of one or more speckle images 202a, 202b and / or over the entirety of one or more speckle images 202a, 202b.

[0073] Another embodiment for determining surface roughness measurements may include providing at least one speckle image 202a, 202b to a data-driven model such as a convolutional neural network (CNN). The data-driven model may receive at least one of the speckle images 202a, 202b in its input layer. The data-driven model may further include one or more hidden and output layers. The speckle images 202a, 202b may be of a predetermined size. The input layer may be specified according to the predetermined size of the speckle images 202a, 202b. The layers of the data-driven model may be connected. Thus, the speckle images 202a, 202b may pass through the layers. In particular, the pixel values ​​associated with the speckle images 202a, 202b may pass through the layers of the data-driven model. While the pixel values ​​may pass through the layers of the data-driven model, the pixel values ​​may interact with each other and / or, preferably, be coupled non-linearly. Additionally or alternatively, the pixel values ​​may be transformed. Preferably, the pixel values ​​may be transformed by the data-driven model into an index of the surface roughness measurement. The index of surface roughness measurements may include surface roughness measurements, and / or surface roughness measurements may be derived from the index of surface roughness measurements. Thus, surface roughness measurements may be received from a data-driven model, and / or a data-driven model may provide surface roughness measurements.

[0074] In one embodiment, the data-driven model may be configured to provide surface roughness measurements, particularly by converting an index of surface roughness measurements into surface roughness measurements.

[0075] The use of data-driven models can be advantageous because these models can learn non-trivial correlations or reflect correlations between different factors that experts would readily consider. Therefore, using data-driven models can reduce investment time while achieving accuracy that surpasses white-box models.

[0076] In one embodiment, a data-driven model provides an index of surface roughness measurements, and / or an index of surface roughness measurements may be received from the data-driven model. From the index of surface roughness measurements, the surface roughness measurements can be derived by mathematical operations and / or a lookup table. For example, the data-driven model may be a classifier that classifies speckle images 202a, 202b into different groups of surface roughness measurements. Thus, the output may show group labels. Group labels may show surface roughness measurements. The relationship between group labels and surface roughness measurements can be defined, for example, by a lookup table. Other embodiments are possible for establishing a relationship between surface roughness measurements and an index of surface roughness measurements.

[0077] For the purposes described above, a data-driven model can be parameterized and / or trained according to a training dataset. The training dataset may include multiple speckle images 202a, 202b and corresponding surface roughness measurements and / or surface roughness measurement indices. The surface roughness measurements and / or surface roughness measurement indices may refer to labels associated with the speckle images 202a, 202b. Parameterization may be a prerequisite for training a data-driven model. A data-driven model can be trained based on the parameterization of the data-driven model.

[0078] Another embodiment for determining surface roughness measurements may include providing speckle images 202a, 202b to a physical model. The physical model may preferably reflect the physical phenomenon in mathematical form, including, for example, a first-principles model. The physical model may include a set of equations that describe the interaction between an object, in particular the surface of an object, and coherent electromagnetic radiation, thereby generating surface roughness measurements. For example, the physical model may include and / or combine at least one of the following relationships related to speckle contrast, speckle modulation, speckle size, fractal dimension, or a combination thereof. The physical model may be a white-box model. The physical model can convert speckle images 202a, 202b into surface roughness measurements. The physical model may linearly combine the above relationships to introduce weightings between, for example, speckle contrast, speckle modulation, speckle size, fractal dimension, etc. Some of the factors described above may be closely related to the surface roughness measurements, while others may be loosely related. Therefore, weighting reflects these relationships and results in higher accuracy. This allows for a more reliable determination of surface roughness, as a single factor alone may not yield a significant result.

[0079] Figure 3 shows an exemplary embodiment of a method for measuring the surface roughness associated with object 300.

[0080] In block 302, a speckle image may be received. The speckle image may be received and / or generated by a camera as described in the context of Figures 1 and 4. Coherent electromagnetic radiation may be emitted from an illumination source as described in the context of Figure 1. Furthermore, the camera and illumination source may be part of a single device and / or system. The generation of a speckle image may be initiated by a user of a mobile electronic device. The user may want to evaluate surface roughness relevant to the user. The user can operate an application. The application may initiate the generation of a speckle image, the reception of a speckle image, the determination of surface roughness measurements, the provision of surface roughness measurements, or a combination thereof.

[0081] Before determining the surface roughness measurement in block 304, the speckle image may be cropped to a predetermined size. This removes the background and increases the degree of speckle related to the object. Speckle related to an object, particularly a living organism such as a human, may refer to speckle caused by coherent electromagnetic radiation illuminating at least a portion of the object. Preferably, the predetermined size may reflect the spatial extent of the object in the speckle image. For example, if the object is related to a circular shape, the speckle image of the predetermined size may be a speckle image of an arbitrary shape that follows the circular shape of the object, or an area related to the circular shape of the object.

[0082] In block 304, the surface roughness measurement may be determined as described in the context of Figure 2. Preferably, the surface roughness measurement may be determined by a processor as described in the context of Figure 1. The processor, camera, and light source may be part of a single device and / or system.

[0083] In block 306, surface roughness measurements can be provided. For example, surface roughness measurements may be provided to an application for a mobile electronic device. Furthermore, the application may be configured to initiate the determination of surface roughness measurements and / or to initiate the generation of speckle images. The application may display the surface roughness measurements, particularly the values ​​of the surface roughness measurements, to, for example, a human. The human may be the user and / or owner of the mobile electronic device. Thus, surface roughness measurements, particularly the values ​​of the surface roughness measurements, may be provided to a human. The application may further process the surface roughness measurements to derive the properties of human skin.

[0084] Figure 4 shows an embodiment of a surface 402 associated with an object. Surface 402 may include a plurality of surface features. Surface features may be lateral surface features 410 and / or vertical surface features 408. Lateral surface features 410 can be quantified according to a dashed line indicating the length of a depression on surface 402. Vertical surface features 408 can be quantified based on a dashed line indicating the height of a ridge on surface 402. This surface 402 may be irradiated by coherent electromagnetic radiation emitted from an illumination source 406, as described in Figures 1 and 3. As described in the context of Figures 1 and 3, a speckle image may be generated using a camera 404 while the surface is irradiated by coherent electromagnetic radiation.

[0085] This disclosure has been described in conjunction with preferred embodiments and examples. However, those skilled in the art can understand and implement other modifications by examining the drawings, this disclosure, and the claims. Of particular note is that each step presented can be performed in any order, i.e., the present invention is not limited to a specific order of these steps. Furthermore, different steps do not need to be performed in a specific location or on one node of a distributed system, i.e., each step may be performed on different nodes using different equipment / data processing.

[0086] In this specification, “determining” also includes “to initiate or cause a decision to be made,” “generating” also includes “to initiate and / or cause a generation to be made,” and “providing” also includes “to initiate or cause a decision, generation, selection, transmission and / or reception to be made.” “To initiate or cause an action to be performed” includes any processing signal that triggers a computing node or device to perform its respective action.

[0087] In the claims and specification, the word “comprising” does not preclude other elements or steps. The indefinite articles “a” or “an” and the definite article “the” do not preclude plural forms. In particular, the indefinite articles “a” or “an” can be replaced by one or more expressions, and the definite article “the” can be replaced by one or more. A single element or other unit can perform the functions of multiple entities or items described in the claims. The mere fact that certain means are described in mutually different dependent claims does not indicate that a combination of these means cannot be used in a favorable implementation.

[0088] The disclosures and embodiments described herein relate to the methods, systems, devices, and computer program elements listed above, and vice versa. Advantageously, the advantages provided by any one embodiment and example apply equally to all other embodiments and examples, and vice versa.

Claims

1. A method for measuring the surface roughness of an object, wherein the method is a) Receiving a speckle image showing an object while it is being irradiated by coherent electromagnetic radiation associated with wavelengths between 850 nm and 1400 nm, b) Determining the surface roughness measurement based on the speckle image, c) To provide the surface roughness measurement values, A method that includes this.

2. The method according to claim 1, wherein the coherent electromagnetic radiation relates to a wavelength between 900 nm and 1000 nm, and / or the coherent electromagnetic radiation relates to a wavelength range between 1100 nm and 1200 nm, and / or the coherent electromagnetic radiation relates to a wavelength range between 1340 nm and 1440 nm.

3. The method according to claim 1 or 2, wherein determining the surface roughness measurement value based on the speckle image means determining the surface roughness measurement value based on the speckle pattern in the speckle image.

4. The method according to claim 1 or 2, wherein the speckle image is generated by a camera including an image sensor, a lens, and a polarizer.

5. The method according to claim 1 or 2, wherein the coherent electromagnetic radiation is patterned coherent electromagnetic radiation, and / or the coherent electromagnetic radiation comprises one or more light beams.

6. The method according to claim 1 or 2, wherein the object is a human being, and the surface roughness is related to human skin.

7. The method according to claim 6, wherein the human being generates a speckle image and / or initiates the generation of the speckle image.

8. The method according to claim 1 or 2, wherein the distance between the object and the camera used to generate the speckle image is between 10 cm and 1.5 m, and / or the distance between the object and the illumination source used to illuminate the object is between 10 cm and 1.5 m.

9. The method according to claim 1 or 2, wherein the surface roughness measurement is determined based on the speckle image by providing the speckle image to a model and receiving the surface roughness measurement from the model.

10. The method according to claim 1 or 2, further comprising reducing the speckle image to a predetermined size before determining the surface roughness measurement.

11. The method according to claim 1 or 2, wherein the surface roughness measurement is determined by a mobile electronic device and / or the speckle image is generated by a camera of the mobile electronic device.

12. The method according to claim 1 or 2, wherein the speckle image relates to a resolution of less than 5 megapixels.

13. A device or system for measuring surface roughness, wherein the system or device is: (a) an irradiation source configured to irradiate an object with coherent electromagnetic radiation relating to wavelengths between 850 nm and 1400 nm, (b) A camera configured to generate a speckle image showing an object under irradiation by coherent electromagnetic radiation related to wavelengths between 850 nm and 1400 nm, (c) A processor configured to receive a speckle image from the camera, determine a surface roughness measurement value based on the speckle image, and provide the surface roughness measurement value, A device or system that includes such devices or systems.

14. Use of a surface roughness measurement obtained by the method of claim 1 or 2, and / or a surface roughness measurement obtained by the system and / or device described in claim 13, for evaluating human surface roughness.

15. A non-temporary computer-readable storage medium, wherein the computer-readable storage medium, when executed by a computer, a) Receiving a speckle image showing an object irradiated by coherent electromagnetic radiation related to wavelengths between 850 nm and 1400 nm, b) Determining the surface roughness measurement based on the speckle image, c) To provide the surface roughness measurement values, A computer-readable storage medium containing instructions that cause something to be executed.

Images