79 results about "Depth imaging" patented technology

The invention discloses a photoacoustic spectrum-confocal Raman spectrum multi-view-field high-dimensional imaging method and a photoacoustic spectrum-confocal Raman spectrum multi-view-field high-dimensional imaging device. Nanosecond pulse laser with adjustable wavelength is generated through a continuous spectrum nanosecond pulser and a narrow-band spectrum tuning module and cooperates with a microscopic detection module, a depth imaging module, a galvanometer system and an electric displacement table to obtain spatial three-dimensional data, a photoacoustic spectrum and a Raman spectrum under multiple view fields; shallow photoacoustic spectrum-Raman spectrum tomography and deep three-dimensional photoacoustic imaging are realized; correcting a photoacoustic spectrum quantitative error by using a spectrum decoupling algorithm, and fusing the multi-view-field data to generate multi-view-field high-dimensional photoacoustic spectrum-Raman spectrum data; high-dimensional data features are extracted based on a 3D convolution instance segmentation network, and quantitative analysis and spatial distribution visualization of blood oxygen, inflammatory factors and the like are achieved. According to the method, the limitation that substances are difficult to analyze in photoacoustic imaging is overcome, the contradiction between the imaging resolution and the penetration depth is solved, and an accurate analysis means is provided for diagnosis of diseases such as knee osteoarthritis and sepsis.

The invention discloses a multi-mode synchronous monitoring method and system based on traditional Chinese medicine pulse conditions and microcirculation, and relates to the technical field of traditional Chinese medicine intelligent diagnosis. A pulse position coordinate set is generated by combining a preset space mapping relation; a pulse pressure signal and a microcirculation blood flow image are synchronously acquired; frequency domain transformation is performed on the pulse pressure signal and a pathological risk probability L is calculated; a comprehensive node feature vector is constructed to form a graph structure; a pathology mode recognition result is output by matching a pathology sub-atlas template library, dynamic correlation analysis of traditional Chinese medicine pulse conditions and microcirculation is achieved, and the technical bottlenecks that traditional pulse diagnosis is high in subjectivity, multi-mode asynchronous and difficult in pathology quantification are broken through.

The invention relates to the technical field of medical imaging, and discloses a peripheral lung cancer pleural invasion evaluation system based on ultrasonic targeted radiography, comprising: a double-frequency ultrasonic probe configured to independently work at different frequencies to realize depth imaging and high-resolution imaging of lung tissues and suitable for different types of lung lesions; the signal processing module can receive, analyze and process echo signals transmitted by the ultrasonic probe, generate images in real time and extract feature information; and the dynamic motion compensation module is used for automatically adjusting the emission time sequence and parameter setting of the ultrasonic signal based on the real-time breathing motion state of the patient. By adopting the design of the double-frequency ultrasonic probe and the core-shell structure, high-resolution imaging and depth detection of lung tissues are realized. By dynamically adjusting the thickness of the probe shell to be matched with the frequency, the system can adapt to complex tissue environments under different physiological conditions, and image definition and stability are ensured.

The invention provides a video SAR depth imaging method and system based on interpretable sparse tensor recovery, and relates to the technical field of synthetic aperture radar imaging, and the method comprises the following steps: building a tensor signal model according to a video SAR working process and an observation model of a single-frame SAR signal; establishing a video SAR sparse reconstruction model according to the established tensor signal model in combination with the low-rank sparse characteristic of the SAR image video; carrying out iterative solution on the established video SAR sparse reconstruction model; constructing a preliminary video SAR depth imaging network by deeply expanding an iterative solution of the video SAR sparse reconstruction model, and performing SAR video image correlation learning and sparse tensor recovery on the preliminary video SAR depth imaging network; and performing network training through back propagation, and optimizing parameters of the preliminary video SAR depth imaging network to obtain a final video SAR depth imaging network. According to the method, the imaging performance under the sparse sampling condition can be effectively guaranteed, and the method has applicability to complex scenes.

Examples are disclosed that relate to motion blur corrections for time-of-flight (ToF) depth imaging. One example provides a depth camera comprising a ToF image sensor, a logic machine, and a storage machine storing instructions executable by the logic machine to receive depth image data from the ToF image sensor, the depth image data comprising phase data and active brightness (AB) data, determine a first two-dimensional (2D) AB image corresponding to a first modulation frequency, and determine a second 2D AB image corresponding to a second modulation frequency. The instructions are further executable to determine a 2D translation based upon a comparison between the first 2D AB image and the second 2D AB image, determine corrected phase data based on the 2D translation to form corrected phase data, perform phase unwrapping on the corrected phase data to obtain a three-dimensional (3D) depth image, and output the 3D depth image.

The application discloses a wide-range noise robust depth imaging method and system based on RGB guidance. The method steps are as follows: constructing a calibrated RGB-D database; establishing a differentiable physical imaging model, and adding physical constraints of an actual scene to obtain multiple noise-free measurement values; constructing a visual TOF double-branch multi-scale fusion depth reconstruction network, taking depth feature extraction and visual feature extraction fusion as an end-to-end network for iterative optimization, and using color pseudo-edge and Fisher information loss to supervise the network reconstruction effect, and jointly training the encoding function and the depth reconstruction network; inputting test data into the trained encoding function and reconstruction network to reconstruct the depth map of the test scene; and building an RGB-guided wide-range noise robust depth imaging hardware prototype system, and using the optimized encoding function and reconstruction network to reconstruct a high-quality depth map in an actual scene.

The invention relates to the technical field of automatic boxing, in particular to a robot automatic boxing method, system and device based on multi-mode perception feedback control, and the method comprises the steps that the geometric dimension of goods to be loaded is obtained; a boxing space coordinate system fixedly associated with the container body is established above the boxing area, surface appearance data of goods placed in the container area are collected in real time, a three-dimensional height distribution diagram is constructed, and a local protruding area is identified based on the three-dimensional height distribution diagram; and on the basis of the local convex area, in combination with the geometric dimension of the to-be-filled goods, planning an optimal box falling position, and recording the optimal box falling position as a box falling area. According to the method, the packing space coordinate system fixedly associated with the box body is established, the stacking state in the box and the goods to be loaded are sensed in real time in combination with three-dimensional depth imaging, the three-dimensional height distribution diagram reflecting the actual morphology in the box is constructed, and on the basis, the physical dimension of the goods and the height distribution information in the box are fused, so that the packing space coordinate system is obtained. And planning a box falling area with the optimal stability.

Examples are disclosed that relate to motion blur corrections for time-of-flight (ToF) depth imaging. One example provides a depth camera comprising a ToF image sensor, a logic machine, and a storage machine storing instructions executable by the logic machine to receive depth image data from the ToF image sensor, the depth image data comprising phase data and active brightness (AB) data, determine a first two-dimensional (2D) AB image corresponding to a first modulation frequency, and determine a second 2D AB image corresponding to a second modulation frequency. The instructions are further executable to determine a 2D translation based upon a comparison between the first 2D AB image and the second 2D AB image, determine corrected phase data based on the 2D translation to form corrected phase data, perform phase unwrapping on the corrected phase data to obtain a three-dimensional (3D) depth image, and output the 3D depth image.

Disclosed is a depth imaging system with a light source; a depth sensor comprising direct Time-of-Flight (dToF) pixels and indirect Time-of-Flight (iToF) pixels; and processor(s) configured to: employ the light source to emit an intensity-modulated light pulse towards objects in a real-world environment; obtain dToF data indicative of time taken by the intensity-modulated light pulse to reach the dToF pixels after being reflected by the objects; obtain iToF data indicative of phase shifts undergone by the intensity-modulated light pulse upon reaching the iToF pixels after being reflected by the objects; determine optical depths for the dToF pixels; determine optical depths for the iToF pixels; and generate a depth image from the optical depths of the dToF pixels and the optical depths of the iToF pixels.

The system comprises a light splitting module, an imaging module and an automatic focusing module, the light splitting module is used for splitting light reflected by a sample into a main light path and a light splitting path, the imaging module is used for obtaining a full depth-of-field image corresponding to the main light path, and the automatic focusing module is used for focusing the full depth-of-field image. The automatic focusing module comprises a liquid lens and an automatic focusing sensor, the liquid lens is located between the imaging module and the light splitting module and is coaxial with the imaging module and the light splitting module, and the automatic focusing sensor is used for obtaining light on a light splitting path and calculating the defocusing amount of the sample; and the control module is used for storing a calibration mapping relation between the driving parameters of the liquid lens and the defocusing amount of the automatic focusing sensor, and is also used for calling the mapping relation table to adjust the driving parameters of the liquid lens according to the defocusing amount. Dynamic electric signals can be generated according to feedback data of the automatic focusing sensor, the liquid lens is driven to achieve millisecond-level focal length switching, high-speed focusing is achieved, and a clear panoramic depth image of a sample is shot.

A method for operating a sparse depth imaging system is presented. The method comprises receiving a depth map of an environment. The depth map comprises a plurality of pixels having locations in an optical sensor coordinate system. A pattern of illuminator dots in an optical source coordinate system is received. Each illuminator dot has a fixed location in a defined plane in the optical source coordinate system. The depth map is projected into a 3D point cloud in the optical sensor coordinate system. Each point in the 3D point cloud is assigned a 2D location in the defined plane. A depth value for each illuminator dot is interpolated based on transformed depth of points in the 3D point cloud. Each illuminator dot is assigned a 3D location in the optical sensor coordinate system. A depth for each illuminator dot is output in the optical sensor coordinate system.

A depth imaging system based on modulated light field, characterized in that it comprises the following modules: a light projection module for projecting discrete light beams to a target object and switching between a modulated light field mode and a uniform light field mode; a light source control module for light emission and control of the switching of the light field mode; a TOF sensing module for receiving the measurement light beams reflected by the target object; a multipath interference elimination module for obtaining image data under the modulated light field mode and the uniform light field mode, estimating the multipath interference component therefrom, removing the measurement error caused by the multipath interference, and outputting a high-precision depth image. The present application can determine the multipath interference component by the background area data in the first image data collected when the discrete light beams are projected to the target object, process the light spot area data to remove the multipath interference component, thereby eliminating the measurement error caused by the multipath interference, and realizing the output of a high-precision depth image.

The application discloses an unsupervised array SAR imaging method with signal-to-noise ratio robustness and target generalization, which is constructed by a deep neural network architecture including two cascaded stages of denoising and reconstruction. In the first stage, the statistical characteristics of the measurement domain are used to remove noise interference, and the pure measurement signal is extracted by using the first stage network. In the second stage, the scene details are recovered by combining the physical forward model and the regularization constraint, and the second stage network is combined with the physical forward projection operator and the regularization constraint. The method overcomes the dependence of the existing deep imaging method on a large amount of high-quality labeled data set, and the defects of high computational complexity and poor noise resistance of the traditional iterative algorithm, and has the characteristics of obtaining high signal-to-noise ratio and high-resolution array SAR images under the condition of only containing noisy observation data, and significantly improving the imaging robustness and generalization ability in complex scenes.

The present disclosure provides new and innovative systems, methods, and apparatuses for detecting non-decode events in indicia scanning settings. In an example, a system comprises a depth imaging assembly, a 2D imaging assembly, and a processing device, wherein the processing device is configured to attempt to decode an indicium affixed to a first item that is passing through at least one of a field of view of the 2D imaging assembly or a field of view of the depth imaging assembly, determine a first characteristic associated with the first item based on depth data from the depth imaging array, and responsive to a failed attempt to decode the indicium, detect a second item that is passing through the field of view of the 2D imaging assembly and the field of view of the depth imaging assembly, determine a second characteristic associated with the second item based on depth data from the depth imaging assembly, determine if the first item is a same item as the second item based on comparing the first characteristic and the second characteristic, and responsive to determining that the first item and the second item are not the same item, transmit a message to an alert module that is indicative of a non-decode event.

Depth images acquired during a scan of one or more portions of a user's body may be used to determine a full-body three-dimensional (3D) representation of the user. Depth imaging data may be used to determine the full-body shape and contours of the user when rotating in front of a user device. Machine learning methods for pose detection may be used to determine the portion of the user's body being scanned, as well as the position and pose of the user's body. A point cloud alignment process that leverages knowledge of user behavior during the scan is used to determine parameters for a full-body representation of a user, which may be based on a plurality of partial scans of the user's body. A full-body representation of a user may be output and / or displayed along with accurate measurements determined for various portions of the user's body.

This invention provides a true surface seismic-geological integrated velocity modeling and pre-stack depth imaging method, belonging to the field of seismic imaging technology. The method includes: obtaining a near-surface velocity model using first-arrival tomographic inversion and fusing it with a mid-deep velocity model obtained from a large floating surface of reflected waves to form an initial depth-migrated velocity field; optimizing the velocity model using reflected wave tomographic velocity inversion to construct a true surface isotropic velocity model; extracting azimuth and dip data from an isotropic migration seismic database, and then calculating various parameters of the anisotropic model and obtaining anisotropic velocities through anisotropic model analysis; and repeatedly iterating and updating the model using network tomographic inversion to obtain a multi-information-constrained anisotropic velocity model. This invention solves the problems of slow modeling speed, poor accuracy, poor imaging quality, and low precision in existing true surface seismic-geological velocity modeling and pre-stack depth imaging methods.

Systems and methods for performing sensor fusion using a depth imager and a radar system involve transmitting radio frequency (RF) energy from the radar system to an area and simultaneously transmitting light to the area using a light source, receiving reflected light at the depth imager aligned with the light source, and receiving RF reflections at the radar system. The reflected light is processed to obtain azimuth, elevation, range, range variance, and reflectivity for each pixel making up the area. The RF reflections are processed to provide azimuth, elevation, range, range variance, velocity, and velocity variance for a subset of pixels representing the area of interest. Performing sensor fusion includes using the azimuth, elevation, range variance, and reflectivity generated by the depth imager and the range, velocity, and velocity variance generated by the radar system.

An imaging device (10) based on multi-photon depth imaging and an imaging probe (100) thereof. By using a split structure, a miniaturized imaging probe (100) is achieved. During imaging, the imaging probe (100) can be fixedly connected to an imaging object (20), and a multi-photon effect is excited inside the imaging object (20) by means of excitation light to acquire optical signals inside the imaging object (20), so as to detect internal conditions inside the imaging object (20).

A substrate analysis system includes a load-lock module configured to load or unload a substrate on which a pattern layer is formed; a milling module configured to form a milling surface from which at least a portion of the pattern layer is removed; a depth measuring module configured to measure a milling depth of an analysis region formed on the milling surface; an imaging module configured to capture a two-dimensional image of the analysis region; and a control module controlling the substrate to circulate through the milling module, the depth measuring module, and the imaging module, when the milling depth is shallower than a set target depth, wherein the milling module adjusts a path of the ion beam so that the ion beam moves horizontally in the milling region according to a scanning profile received based on an intensity map of the ion beam.

A method for operating a depth imaging system is presented. The method comprises illuminating an environment using an optical source configured to output modulated structured light comprising a pattern of dots. Reflected illumination is received at an optical sensor comprising a 2D pixel grid. Received reflected illumination from the 2D pixel grid is downsampled to a plurality of 1D dot trail vectors. For each dot trail vector, a depth is indicated based on a time-of-flight depth measurement and a triangulation factor.

Disclosed are an imaging device based on multi-photon depth imaging and an imaging probe thereof, and a miniaturized imaging probe is implemented by using a separated structure. During imaging, the imaging probe may be fixedly connected to an imaging object, and an optical signal inside the imaging object may be collected by exciting a multi-photon effect inside the imaging object by excitation light to detect an internal status in the imaging object.

The invention provides a true surface seismic geology integrated velocity modeling and pre-stack depth imaging method, and belongs to the technical field of seismic imaging, and the method comprises the steps: solving a near-surface velocity model through the tomography inversion of a first-motion wave, and fusing the near-surface velocity model with a mid-deep layer velocity model obtained by a large floating surface of a reflected wave, so as to form an initial depth migration velocity field; the velocity model is optimized through reflection wave tomography velocity inversion, and a true earth surface isotropic velocity model is built; the method comprises the following steps of: extracting azimuth angle and dip angle data of an isotropic migration seismic database on the basis of the isotropic migration seismic database, analyzing through an anisotropic model, calculating various parameters of the anisotropic model, solving an anisotropic speed, and repeatedly iterating and updating for multiple times through a network tomography inversion method to obtain a multi-information constraint anisotropic speed model; according to the method, the problems of low modeling speed, poor accuracy, poor imaging quality and low precision of the existing true surface seismic geological velocity modeling and pre-stack depth imaging are solved.

Embodiments of the present application relate to the field of analyzing materials by optical means, and particularly to a method for determining hyperspectral images of a core half-cylinder. The method provided by the embodiments of the present application calibrates a spectral imaging device and a depth imaging device, and associates the depth values and spectral pixel data obtained by the two imaging devices, so as to determine the accurate transformation relationship between the three-dimensional coordinates obtained by the depth imaging device and the two-dimensional pixel coordinates obtained by the spectral imaging device, to realize spatial registration and fusion of core data collected by different devices, and to facilitate subsequent unified analysis of various core data. The real-time collection of hyperspectral data and depth data of the core half-cylinder by the two imaging devices can provide a reference for core curved surface identification and scanning area positioning, and can also assist in the correction of reflectivity data of the core half-cylinder, reduce the reflectivity calculation error, and help to obtain more accurate hyperspectral images of the core half-cylinder.

A device, system, method (100) and computer-program product are disclosed for assisting in the positioning of objects, including a subject and / or auxiliary equipment, on a subject support in an imaging and / or therapy session. The method comprises acquiring (101) image / spatial data that comprises depth / 3D information, using a camera system, of the subject support having (an) object(s) placed thereon. The method comprises obtaining (111) reference data, comprising reference depth / 3D information, representative of the subject support without the object(s) placed thereon. From the image and / or spatial data, at least one geometric attribute of the (or each) object is determined, in which this determining (104) comprises detecting (105) the object(s) by determining (110) 3D points for which the acquired depth / 3D information differs from the reference depth / 3D information and associating these points with the object. The geometric attribute of each object is determined by directly calculating sample statistic(s) of the points associated with the object.

The invention relates to the technical field of petroleum geophysical exploration, and particularly discloses a high-precision seismic imaging method and device for a special geologic body, and the method comprises the steps: carrying out the combined modeling of a plurality of logging wells in a work area, and fitting an imaging constraint factor; performing iterative optimization on the original imaging speed model by taking the imaging constraint factor as a constraint factor; if an iterative optimization result converges, taking the imaging speed model after iterative optimization as a final speed field, carrying out TTI depth domain migration imaging, and outputting imaging data; and if the iterative optimization result is not convergent, re-fitting the imaging constraint factor until the iterative optimization result is convergent. According to work area multi-well combined modeling, special geologic body imaging factors are fitted, imaging constraint factors serve as constraint factors, an original imaging speed model is optimized and adjusted, after iteration, a newest speed achievement model is output, the speed model serves as a final speed field, limitation of conventional depth imaging is avoided, the result is easy to converge, and the method is suitable for large-scale popularization and application. And the well determination accuracy is high.

The embodiment of the invention relates to the field of analyzing materials by using optical means, in particular to a method for determining a hyperspectral image of a semi-cylindrical surface of a rock core. According to the method provided by the embodiment of the invention, a spectral imaging device and a depth imaging device are adopted and calibrated, and depth values obtained by the two imaging devices are associated with spectral pixel data; the accurate transformation relation between the three-dimensional coordinates obtained by the depth imaging device and the two-dimensional pixel coordinates obtained by the spectral imaging device can be determined, space registration and fusion of core data collected by different devices are achieved, and unified analysis of multiple types of subsequent core data is facilitated. The hyperspectral data and depth data of the core semi-cylindrical surface are collected in real time through the two imaging devices, reference can be provided for core curved surface form recognition and scanning area positioning, meanwhile, correction of the reflectivity data of the core semi-cylindrical surface can be assisted, the reflectivity calculation error is reduced, and the calculation accuracy is improved. And a more accurate hyperspectral image of the semi-cylindrical surface of the rock core can be obtained.

This invention discloses a three-dimensional electrical resistivity tomography (EDT) method for detecting cavities based on T-shaped electrode survey lines. A two-dimensional T-shaped electrode survey line advance detection observation system is constructed by deploying two mutually perpendicular survey lines (line 1 and line 2) behind the tunnel face. Electrode electrical resistivity data are collected for both vertical and parallel tunnels, and resistivity depth imaging maps are then established for each. The vertical imaging map determines whether the cavity is located to the left or right of the tunnel face, while the parallel imaging map determines whether it is located in front of or behind the tunnel face. This achieves preliminary two-dimensional planar positioning of the cavity. The detection space around the tunnel face is divided into target units, and through resistivity forward modeling and correlation calculation, correlation imaging results of each target unit are obtained. Ranges exceeding a threshold are delineated in the map, and combined with the preliminary positioning, the three-dimensional spatial location of the cavity is accurately determined, ensuring safe tunnel excavation.

The invention relates to a near-infrared band ratio type rare earth fluorescent nanoprobe as well as a preparation method and application thereof. The ratio-type rare earth fluorescent probe is a nanocrystal with a core-shell structure, an inner core layer of the ratio-type rare earth fluorescent probe is a light-emitting layer which absorbs laser radiation and emits a near-infrared fluorescent wave band, a middle shell layer of the ratio-type rare earth fluorescent probe is a light-emitting shell layer which absorbs laser radiation and emits another near-infrared fluorescent wave band, and an outer shell layer of the ratio-type rare earth fluorescent probe is an inert shell layer. Wherein the inner core layer takes Tm < 3 + > as a light-emitting center, and the light-emitting shell layer takes Er < 3 + > as a light-emitting center. Through difference response of two fluorescence signal wave bands to water absorption, an index relation between ratio-type fluorescence signal intensity and tissue penetration depth is established, and noninvasive depth positioning and imaging of a mouse living body are realized. The fluorescent nanoprobe can be used for depth positioning of blood vessels of the brain, the abdomen and the legs of a mouse and continuous monitoring of a dynamic migration process from the blood vessels to bones, and has a wide application prospect in the aspect of in-vivo ratio depth imaging.

The application discloses a data-driven TOF depth imaging method capable of removing multipath error. The steps are as follows: three-dimensional modeling is carried out for a scene with multipath effect, depth rendering is carried out on the scene to obtain a depth map label and corresponding scene parameters; the scene is described in the form of a triangular mesh, transient rendering based on pbrt is carried out to obtain a pulse response transient map; a learnable modulation function is convolved with the pulse response transient to obtain echo after the modulation function illuminates the scene; a learnable TOF demodulation function is used, multiplied with the output of the previous step, integrated and added with ambient light and noise, and input into a reconstruction subnetwork; the network is trained, a modulation function is obtained, laser is modulated, the scene is illuminated, echo is measured by a photodetector, multiplied with the obtained demodulation function and filtered; and a voltage value is input into the trained reconstruction subnetwork to obtain a depth map. The method enhances the robustness of TOF depth imaging to multipath error and improves imaging accuracy.

The invention provides an image generation device, a head-up display and a vehicle. The image generation device comprises a laser, a scanning module and a developer. The scanning module is arranged on a light emitting path of the laser, and the developer is arranged on a light emitting path of the scanning module. The laser is configured to emit a laser beam; a plurality of continuous imaging planes are arranged in the imaging device in the first direction, the imaging planes are perpendicular to the first direction, and fluorescent materials are arranged in the imaging planes; the scanning module is configured to control the focal point of the laser beam to move in the first direction so that the laser beam can be focused on the target development plane, and control the focal point of the laser beam to conduct two-dimensional scanning in the target development plane so that the laser beam can excite the fluorescent material in the target development plane to generate an image light beam with two-dimensional image information. The image generation device can form a three-dimensional image with real depth information, and can realize multi-depth imaging and three-dimensional display when being subsequently applied to a head-up display.