46 results about "Reflectance map" patented technology

The invention discloses a high-speed camera for semiconductor graph-free dark field detection and a detection method, and relates to the technical field of semiconductor detection, and the method comprises the steps: integrating an adjustable laser light source array, a high-performance TDI camera and a pre-scanning sensor, carrying out the brightness information collection of a pre-scanning region, and generating a pre-scanning reflectivity graph; sequentially irradiating a pre-scanning area with different illumination light through an adjustable laser light source array, generating a plurality of scanning images, and extracting features to generate a multi-modal differential feature field; predicting a main defect type in the region through a multi-mode illumination and defect feature collaborative enhancement algorithm to generate a corresponding illumination mode instruction; calculating an optimal TDI parameter through a TDI parameter dynamic modulation algorithm to generate a TDI camera control instruction; and scanning the wafer according to the two instructions, collecting original image data, inputting the original image data into the depth defect classification model, and outputting a defect information graph and corresponding information. The invention aims to solve the problem that semiconductor detection precision and efficiency are difficult to balance.

The invention relates to the technical field of defect detection, in particular to a laser remelting surface defect area segmentation method which comprises the following steps: S1, acquiring a laser remelting surface digital image; s2, performing illumination reflection separation processing on the surface digital image to obtain a reflectivity image, and performing highlight detection and suppression on the reflectivity image to obtain a highlight suppression image; s3, on the highlight suppression image, estimating the surface dominant texture orientation and consistency of the highlight suppression image based on neighborhood gradient statistics; by processing the digital image of the laser remelting surface, the image quality is improved from the source, background texture interference is specifically eliminated, fine defect features are intelligently enhanced, and finally high-precision and robust segmentation of the laser remelting surface defects is realized through global optimization and refined post-processing. The problems of low detection precision and poor reliability caused by surface characteristics in the prior art are effectively solved.

The invention discloses a hydraulic concrete structure underwater surface crack detection method, and relates to the technical field of image sensors, and the method comprises the steps: carrying out the transmissivity mapping and background radiation field separation of underwater light field characterization parameters, generating a target reflectivity image, carrying out the multispectral-polarization data fusion of the target reflectivity image, and generating an enhanced feature image; performing multi-scale convolution kernel space transformation and feature pyramid fusion on the enhanced feature image to generate a crack response diagram, and performing anisotropic non-maximum suppression and morphological skeleton refinement on the crack response diagram to generate crack detection data; and performing geometric topology joint optimization and parameter quantification on the crack detection data, and outputting a structured detection report. According to the invention, by constructing the light field transmission model and synchronously carrying out polarization and depolarization ratio combined noise filtering and geometric distortion correction, adaptive noise filtering is realized, and the measurement precision is improved.

Embodiments herein provide a method and system for a vicarious calibration of optical data from satellite sensors for urban scene flat fields. Identifying test sites automatically in the urban scene helps in vicarious calibration or on-board calibration of the hyperspectral / multispectral image. An internal average relative reflectance is calculated to get a relative reflectance of the image. Band ratios for various pixels is determined to assess flatness of the spectrum. Flat field candidates are identified from the various pixels having average band ratio nearing zero and a morphological technique is applied to determine a flat field. Finally, the image is calibrated vicariously based on the determined flat field as a test site. The on-board calibration of the remote sensing image may lead to a faster way to get the reflectance image of the scene, with the help of the calibration constants.

The invention discloses an image fusion method and system with low light enhancement and overexposure suppression, and belongs to the technical field of image processing, and the method comprises the steps: extracting a reflectivity feature map, a visible light feature map and a dark channel feature map of a visible light image and an infrared feature map of an infrared image in the training process of an image fusion model; obtaining an enhanced reflectivity image according to the reflectivity characteristic pattern; fusing the reflectivity feature map, the visible light feature map and the infrared feature map, and then performing color space conversion to obtain a color fused image; determining an overexposure area and a non-overexposure area of the dark channel feature map; replacing the overexposure area with the features in the infrared feature map, and replacing the non-overexposure area with the features in the enhanced reflectivity image to obtain an overexposure suppressed fusion image; and the fusion image subjected to overexposure suppression participates in model loss calculation. The quality of the color fusion image is improved, and the technical problem that the image fusion quality is affected by overexposure at present is solved.

A precipitation scanning aperture RADAR system comprises at least one transmitting satellite (TX) and a plurality of receiving satellites (RXs) flying in a multi-static formation. The TX emits fixed frequency, pseudo-random, non-repeating phase modulated RADAR energy. Range and Doppler information is extracted from reflected signals received by the RXs to form coherent 2D along track / vertical images, which are combined into a 3D reflectivity map using a power-based incoherent modeling algorithm, such as a Gaussian mixture model. Embodiments implement a spotlight mode in which, for each RX, a plurality of the coherent 2D images obtained during successive coherent processing intervals (CPIs) are incoherently combined, and the satellites adjust their orientations between each CPI to compensate for satellite movement. Inter-satellite timing synchronization of 10 ns or better is sufficient for forming the 3D reflectivity map. The range and Doppler information can be extracted by concurrently demodulating at a plurality of reference frequencies.

The invention discloses a defect identification method and device based on multi-modal data of a chip preparation process, and the method comprises the steps: obtaining a graphite carrier image after epitaxial growth is completed, and generating a graphite carrier and wafer mapping graph; the method comprises the following steps of: performing multi-modal imaging on a wafer to be detected, acquiring a gray scale image, a reflectivity image and a photoluminescence intensity image, synchronizing according to a pixel level, respectively performing normalization processing, and calculating a physical normalization factor according to a normalization result to obtain a multi-modal image; constructing a wafer grid space database, and storing abnormal areas in the multi-modal image, the electrical test data and the graphite carrier image into the wafer grid space database; and based on the mapped multi-modal image, the electrical test data and the multi-source data of the abnormal region in the graphite carrier image, identifying the defect type according to a preset rule. The problem that in the prior art, due to the fact that data generated in the chip manufacturing process are mutually independent, the defect root cause analysis efficiency is low is solved.

A method for performing point-to-point whiteboard parameter ratio correction on a hyperspectral image includes: capturing hyperspectral data of a standard reference whiteboard in advance as white(x,y,w), and storing the records, then captures hyperspectral data of a sample as sample(x,y,w); then selecting an unobstructed and unshaded whiteboard area within a certain range of the hyperspectral data sample(x,y,w) of the test sample, and labeling the area as Area A; calculating a spectral average of the ROI on the sample image as SA(w); calculating a spectral average of whiteboard data in the same position as the ROI as WA(w); dividing the two spectral averages, and obtaining a correction coefficient alpha(w)=WA(w). / SA(w); multiplying the whiteboard parameter ratio correction coefficient alpha(w) by a sample reflectance image matrix after whiteboard parameter ratio correction to obtain a final hyperspectral reflectance image matrix REFL(x,y,w)=alpha(w).*sample(x,y,w). / white(x,y,w).

The invention relates to the technical field of image detection, and discloses a floor processing quality detection method and system based on machine vision. The method comprises the following steps: collecting multiple frames of images of the surface of a floor through multi-angle structured light illumination, constructing a pixel-level luminosity response vector, and decomposing the pixel-level luminosity response vector into a diffuse reflectance graph, a specular reflectance graph and a normal vector graph based on a physical illumination model; and respectively detecting material flaws, coating unevenness and geometric scratches and pits in each component graph by adopting a targeted algorithm, and finally fusing results to generate a high-precision detection report. The system comprises a high-speed camera and a dome-type multi-light-source module which are synchronously controlled, a data processing unit and a defect decision fusion module. According to the method, interference suppression is realized through physical attribute decoupling, and the accuracy and robustness of defect identification are remarkably improved.

The invention is suitable for the technical field of color management, and provides an inkjet color calibration method and system based on material characteristics, and the method comprises the steps: obtaining a multiband reflectivity image of the surface of a to-be-inkjet substrate through multispectral imaging; extracting material features of the base material from the reflectivity image; acquiring ambient light spectrum data, and correcting the color appearance model; based on the material features and the corrected color appearance model, mapping a target color space to a device-related color space through a color conversion model, and generating a color conversion lookup table; according to the material characteristics, ink jet parameters are optimized; and according to the color conversion lookup table and the optimized inkjet parameters, controlling inkjet equipment to execute inkjet. According to the invention, the technical problems of color distortion, low calibration efficiency and inconsistent colors of multiple devices during cross-material painting due to the fact that the optical property difference of materials is not considered in the prior art are solved.

The application discloses a satellite remote sensing data collection and preprocessing method and system, and relates to the technical field of satellite navigation. The method specifically comprises the following steps: collecting remote sensing images of a semiarid region through a satellite, analyzing pixel radiation and brightness gradient to identify bare land water features, constructing a leading atlas, extracting a boundary and registering to generate a standard structure, combining a climate model to correct to generate a reflectivity layer, transferring instructions when parameters are insufficient, reconstructing a queue by using a shortest algorithm to complete radiation calibration, atmospheric correction and geometric registration, and forming a preprocessing layer set. In the application, multi-band division is combined with radiation gradient calculation to enhance the separation degree of ground objects, boundary feature extraction and geometric registration are used to eliminate imaging distortion, a dynamic radiation model is fused with climate parameters to correct reflectivity, a double-threshold value is monitored to optimize a transmission strategy and avoid interruption, a priority algorithm is used to reconstruct a queue to reduce data delay, multi-level quality control is used to cooperatively process to form standard data, and the temporal and spatial reliability and application value are improved.

The invention relates to the technical field of intelligent inspection and state diagnosis of power equipment, in particular to an intelligent recognition system for an abnormal state of a transformer. The system comprises a data acquisition unit, an environmental parameter estimation unit, an optical decoupling unit, a state diagnosis unit, an interference quantification unit and a coupling diagnosis unit. The method comprises the following steps: capturing a high-dynamic pixel flow and estimating environment parameters by a system; the core of the method is that reverse optical transmission decoupling is adopted, and a pixel stream is physically decomposed into an equipment inherent reflectivity graph and an environment illumination and interference graph; the system carries out defect diagnosis on the reflectivity graph, meanwhile, the interference graph is quantified to evaluate the credibility, and finally an interpretable diagnosis report is generated through coupling; according to the method, the problem that a traditional model cannot distinguish the equipment state from environment interference is solved, and the diagnosis robustness is improved.

A non-invasive device for detecting Alzheimer's Disease-associated pathologies, comprising a fundus camera comprising a spectral reflectance imaging unit that includes a broadband light source and a light sensor and a lens assembly to focus light from the broadband light source onto a fundus of an eye, and one or more processors to detect reflected and / or backscattered light from the eye, illuminated by the broadband light source, using the light sensor for determining spectral reflectance information, generate a plurality of spectral reflectance maps comprising counts of reflected and / or backscattered light at a different wavelength or a different range of wavelengths, assign a weight to each of the spectral reflectance maps, and determine one or more regions of interest from the weighted spectral reflectance maps as being a potential Alzheimer's Disease-associated pathology based on a detection of one or more biomarkers indicative of Tauopathy.

A method for identifying a hand is provided. The method includes obtaining, via a ToF sensor of the electronic device, at least one image, wherein the at least one image includes a hand, and the TOF sensor emits infrared (IR) light and receives IR reflection of the IR light emitted from the ToF sensor, identifying a plurality of key points associated with the hand in the at least one image, generating a reflectivity map of the hand based on the reflection of the IR light received by the ToF sensor, identifying a plurality of regions of the hand in the at least one image using the reflectivity map and the plurality of key points, and based on the identified plurality of regions, identifying the hand as either a left hand or a right hand.

The application discloses a target micro-change monitoring method based on a multi-task neural network, and the target scene is photographed under multiple illumination conditions at two observation time points to obtain a current image set and a reference image set respectively; the surface structure diagram and the reflectivity diagram of the scene at the current time point and the surface structure diagram and the reflectivity diagram of the scene at the reference time point are calculated by using the current image set and the reference image set respectively; the obtained two groups of surface structure diagrams and reflectivity diagrams are aligned respectively; the aligned two groups of surface structure diagrams and reflectivity diagrams are input into a multi-scale change monitoring model, and finally the micro-change detection result of the target is obtained. Compared with the prior art, the application detects the change of the scene according to the three-dimensional information of the target and the reflectivity diagram, is not affected by the difference of the illumination conditions during imaging, can more accurately find the subtle change of the target, and is especially suitable for the tasks with high monitoring precision requirements such as cultural relic monitoring and precision instrument monitoring.

The invention relates to the technical field of spectral analysis and detection, and discloses a method and a system for rapidly detecting pesticide residues in food based on multispectral imaging. The method comprises the following steps: acquiring a multispectral image and correcting to obtain a reflectivity image cube; inputting into a variational auto-encoder for encoding to obtain a potential feature vector; after normalization, calculating a mahalanobis distance, screening residual candidate areas, generating space and spectrum attention weights, and performing outer product to construct a collaborative attention tensor; multiplying with the reflectivity image element by element to obtain an enhanced image, extracting features, and inputting the features into a regression network to predict the pesticide residue concentration. According to the invention, the accuracy and generalization ability of pesticide residue detection under different food varieties are improved.

The present application relates to the field of image processing and computer vision, and discloses an unsupervised low-light image enhancement method and system, the method comprising the following steps: S1, constructing a diffusion model; S2, training the diffusion model using pairs of low-quality images in a public dataset and the sum of four loss functions; S3, training the diffusion model using any non-paired low-light image, normal-light image and the sum of two loss functions; S4, using the reflectance map of the low-light feature and the illumination map of the normal-light feature as the diffusion model, and under the guidance of the low-light feature, the enhanced feature is obtained by restoration, and the enhanced feature is used as the input of the decoder to reconstruct the final enhanced image. The system comprises a model construction unit, a model training unit and an enhanced image output unit. The present application also discloses an electronic device and a computer readable storage medium. The present application is used for low-light image enhancement by using traditional retinal theory and deep neural network.

The invention relates to the technical field of spectral reflectivity, in particular to a coal surface reflectivity enhancement method based on multi-angle spectral consistency constraint, which comprises the following steps of: synchronously acquiring multi-angle spectral data by a plurality of unmanned aerial vehicles, constructing a spatial registration image group, extracting consistent pixels to construct an angle reflection structure, and identifying reflection difference wave bands and directions. And executing directivity correction, and fusing to generate an enhanced reflectivity image result. The method comprises the following steps: acquiring a multi-angle spectral reflectivity image group through a plurality of unmanned aerial vehicles, establishing a pixel corresponding relation in combination with time stamps and spatial registration, aggregating reflectivity values at the same position according to angles to form a directional structure, identifying an abnormal wave band region through difference calculation, and processing angle differences in combination with spectral sequence rearrangement and direction correction, so as to identify the abnormal wave band region. A unified reflectivity value set is constructed, a two-dimensional image matrix is generated through spatial splicing and fusion, and the spatial consistency and direction adaptability of coal area multi-angle spectral data are enhanced.

The application relates to the technical field of spectral analysis detection, and discloses a food pesticide residue rapid detection method and system based on multispectral imaging. The method comprises the following steps: collecting a multispectral image and correcting to obtain a reflectivity image cube; inputting a variational autoencoder to encode to obtain a latent feature vector; calculating Mahalanobis distance after normalization to screen a residue candidate area, generating spatial and spectral attention weights and performing outer product to construct a collaborative attention tensor; multiplying the reflectivity image element by element to obtain an enhanced image, and extracting features to input a regression network to predict a pesticide residue concentration. The application improves the accuracy and generalization ability of pesticide residue detection under different food varieties.

The application discloses a panoramic image intrinsic decomposition method based on light pre-extraction and belongs to the technical field of image intrinsic decomposition.The application combines the 360-degree expression of a panoramic image to a scene, saves complex light information onto a pre-extraction image through light pre-extraction, directly uses the pre-extraction image as a constraint condition, can realize accurate positioning of a complex light region, improves the decomposition process through the constraint of the complex light region, reduces highlight and shadow information in a reflectivity image, realizes separation from light properties, introduces the pre-extraction image in the determination of a non-local constraint weight, combines the weight with a KNN result, can effectively select appropriate pixel pairs for constraint, improves the adaptability of constraint between different pixel points in a scene, reduces unnecessary constraint, and improves the image quality obtained through decomposition.The application is suitable for the fields of image decomposition and image processing and improves image processing quality.

A light-emitting display device according to one or more embodiments of the present invention may include: a substrate comprising a plurality of sub-pixels, the plurality of sub-pixels having light-emitting regions and non-light-emitting regions; data lines and gate lines disposed in the non-light-emitting regions on the substrate and intersecting each other; at least one insulating layer disposed on the data lines and the gate lines; and a color filter disposed on the at least one insulating layer and corresponding to at least one sub-pixel of the plurality of sub-pixels, wherein one or more of the plurality of sub-pixels may include a low-reflectivity pattern overlapping the light-emitting regions.

The application relates to a three-dimensional radar echo extrapolation method based on a deformation convolution dynamic field constraint, which comprises the following steps: S1, continuously collecting radar reflectivity and radial velocity data within 2 hours; S2, converting the radar reflectivity data into three-dimensional data in a Cartesian rectangular coordinate system, mapping the radial velocity data into a radial rectangular coordinate system and constructing a sample set; S3, acquiring middle-layer radial convergence feature information in a radial velocity graph; S4, building a radar echo extrapolation model; S5, introducing deformation convolution to automatically extract the dynamic field information of a convection system; S6, dividing the sample set into a training set, a verification set and a test set; and S7, obtaining a reflectivity image in the next 1 hour. The application fully considers the three-dimensional structural features and dynamic characteristics of a weather system, simultaneously combines multi-source physical constraint information, and realizes accurate simulation and prediction of a complex evolution process of the weather system.

This invention discloses a defect identification method and apparatus based on multimodal data from the chip fabrication process. The method includes: after epitaxial growth, acquiring a graphite carrier image and generating a graphite carrier-wafer mapping map; performing multimodal imaging on the wafer under test, acquiring grayscale images, reflectivity images, and photoluminescence intensity images and synchronizing them at the pixel level, performing normalization processing on each, and calculating a physical normalization factor based on the normalization results to obtain the multimodal image; constructing a wafer grid space database, storing the multimodal image, electrical test data, and abnormal regions in the graphite carrier image into the wafer grid space database; and identifying defect types according to preset rules based on the multi-source data of the mapped multimodal image, electrical test data, and abnormal regions in the graphite carrier image. This invention solves the problem of low efficiency in defect root cause analysis in the prior art due to the independent nature of the data generated during the chip fabrication process.

The application discloses an abnormal defect detection method for terminal welding, and relates to the technical field of image data processing, and comprises the following steps: image acquisition is performed through a multi-light-source image acquisition module to obtain a single-light-source image and a uniform light-source image; a reflectivity image is obtained by calculation through the single-light-source image, and a reflected gray-scale image is obtained through bilateral filtering of an enhanced kernel function and nonlinear transformation; Fourier transformation is performed on the reflected gray-scale image, and a frequency domain interest image is obtained through mean filtering and subtraction; two-dimensional interest image is obtained through feature extraction by binary matrix sampling and a conditional probability model; a texture interest image is obtained through twice pixel point screening and maximum value suppression on the reflected gray-scale image; HSV color space conversion is performed on the uniform light-source image, and a color interest image is obtained through Gaussian filtering and a norm distance model calculation; a defect feature image is obtained through weighted fusion, threshold segmentation is performed on the defect feature image, defects are marked, and identification of terminal welding defects is completed.

The application relates to the technical field of computer vision, and discloses a nut growth monitoring method based on multispectral imaging and light-weight AI. Multispectral collection data and edge running conditions of a nut orchard are acquired, valid reflectivity images are generated through dark field deduction, whiteboard calibration and invalid observation marking, and edge executable waveband combinations are screened according to physiological response sensitivity, waveband redundancy degree and edge running conditions. Further, reflectivity description, local texture description and fruit spatial distribution description are extracted, and growth heterogeneity characteristic results are generated through fusion. The growth heterogeneity characteristic results are input into a light-weight monitoring network, physiological consistency constraints are introduced, and standardized growth parameters are obtained through training. The trained network is subjected to low-bit quantization, and is deployed to an edge chip according to the quantization consistency results, so that nut growth monitoring results are output. The application can reduce multispectral input redundancy and edge end reasoning load, and improve the stability of growth output after quantization deployment.

The application discloses a kind of camera and solid-state laser radar online calibration method of mobile robot, first by solid-state laser radar and camera acquisition point cloud and image data, using specific function optimization to subpixel level accuracy, then transform into least square problem and obtain offline external parameter solution.When the speed of mobile robot is lower than threshold value, radar and camera collect current scene point cloud and image original data, project point cloud on camera imaging plane by offline external parameter, generate point cloud reflectivity map with point cloud reflectivity as pixel, convert camera original image to gray scale image, extract the outline pixel of reflectivity map and gray scale image, construct error equation according to the distance of paired points and convert to least square problem, finally solve and obtain online external parameter update.Not only can the camera and solid-state laser radar of mobile robot be calibrated offline external parameter, but also can update external parameter calibration online during robot movement, so as to ensure the stability of subsequent operation of mobile robot.

The application discloses a kind of repair and 3D reconstruction method and system based on contaminated face picture, it is related to artificial intelligence technical field, including: obtaining and preprocessing the contaminated image containing real prototype face image;Through the generator-discriminator network of preestablished, the face image after pre-processing is handled;Initial generation prototype face image is merged with the public face dataset after pre-processing as input image, and the input image is processed according to the parallel encoder network, to obtain initial 3D face model, depth map and reflectivity map;Depth reflection loss function is established based on depth map and reflectivity map;And generator and discriminator are trained based on the generator loss function after updating and the preset discriminator loss function respectively, to output target 3D face reconstruction model.The application solves the problem that the detail fidelity and realism of 3D reconstruction result of single contaminated picture are poor.

Methods of seismic data processing employ neural networks and use a reflectivity image based on the acquired seismic data to generate output training datasets. The neural networks thus trained are used for generating production datasets, without ghosts, source effects, multiples and / or populating a predetermined set of bins in inline-crossline plane for a set of offset classes.

The invention provides an aero-engine component key defect three-dimensional size quantification method and system, and the method comprises the steps: collecting a video sequence when a monocular endoscope moves in an engine, inputting the video sequence with continuous frames into a depth estimation network and a pose estimation network, obtaining a single-frame image depth map and inter-frame pose transformation respectively; calculating luminosity re-projection loss according to the single-frame image depth map and inter-frame pose transformation; deriving a surface normal graph based on the depth map of the single-frame image, generating a composite image through a differentiable physical renderer in combination with the predicted reflectivity graph and light source parameters, and calculating physical rendering loss; and taking the weighted sum of the luminosity reprojection loss and the physical rendering loss as a total loss function, and performing forward propagation on a new input image based on the trained depth estimation network to obtain a depth map. According to the method, a real 3D label is not needed, strong reflection and illumination interference can be overcome, and high-precision three-dimensional size quantification can be realized only through a single hole detector video stream.