1580 results about "Pixel based" patented technology

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention relates to the technical field of image data processing, and discloses a fine metal powder defect detection method and system based on image recognition, and the method comprises the steps: illuminating a metal powder region through a non-polarized light source, and collecting two images in an orthogonal polarization state through imaging equipment with a linear polarizer, according to the method, the defect morphological fingerprints are constructed through the polarization information entropy, the three-dimensional structure information is converted into light field order degree change for detection, the dimension limitation of traditional two-dimensional imaging is broken through, and the detection accuracy is improved. Hidden defects such as internal pores and aggregates can be accurately identified, and meanwhile, stable detection and material defect cross discrimination under a dynamic working condition are realized in combination with motion compensation and multiband entropy ratio verification.

The invention provides a metal product surface flaw detection method and system based on machine vision, and belongs to the technical field of machine vision. The method comprises the following steps: constructing a multi-modal image data set through bright field image acquisition, dark field image acquisition and three-dimensional point cloud data acquisition of the surface of a detected metal product, and registering the multi-modal image data set; based on the registration multi-modal image data of pixel-level alignment, pixel-level defect segmentation is carried out by adopting a double-flow diffusion Transform model, and a defect segmentation map is output; and on the basis of the defect segmentation image and the registered multi-modal image data, through segmentation image binaryzation and region extraction, multi-dimensional defect feature extraction and quantification, defect classification and report generation, a final defect detection report is output, and the metal product surface defect detection method is completed. The invention effectively solves the core pain points of low precision, poor robustness and insufficient generalization ability for unknown defects in metal surface flaw detection, and provides an automatic detection method with high reliability and high precision.

The invention discloses a remote sensing image pseudo label generation method based on prototype perception learning, and a weak supervision change detection model training and detection method, and belongs to the field of remote sensing image change detection. The invention aims to solve the problem that a category activation graph is incomplete in existing remote sensing image weak supervision change detection and the problem that an obtained detection model is unstable in change recognition. According to the method, feature maps of different scales before and after change are extracted from remote sensing images before and after change through a pseudo label generation model, change features are obtained, a class activation mapping map and a class activation mapping map of an unchanged class are obtained, and the class activation mapping map is segmented into response area masks through a threshold value and used for performing weighted aggregation on fusion features to obtain a class prototype; generating an enhanced class activation mapping graph based on the fusion feature and the class prototype, and selecting the class with the highest similarity as a pseudo tag of the pixel; and training based on the pseudo label data of the pixels to obtain a change detection model, wherein the model is used for remote sensing image change detection.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a transformer fault diagnosis method and system based on image recognition, and the method comprises the steps: collecting a transformer multi-mode image sequence in real time, and carrying out the definition and part integrity evaluation and screening to form an initial image set; performing multi-scale space registration on the initial image set and a transformer normal state standard template to generate a reference image, and reversely deriving a displacement vector field based on pixel-level difference; carrying out smooth optimization and geometric reconstruction on the displacement vector field under the geometric constraint of the transformer structure, and generating a correction image with a real structure; fault feature enhancement is carried out in a gradient domain of the corrected image, a fault area is identified through matching of multichannel feature extraction and a transformer typical fault feature library, and a diagnosis report integrating fault types, confidence coefficients and geometric parameters is generated; according to the method, the problem of image geometric deformation caused by shooting condition differences is effectively solved, and the accuracy and reliability of fault identification are improved.

The invention relates to the technical field of data processing, and discloses a visual data comprehensive processing method for automobile industrial data based on digital twinning. The method comprises the following steps: performing recursive quartering on a three-dimensional space of an automobile general assembly workshop through a workshop process pixel quadtree index algorithm, performing process type marking on multi-source heterogeneous data according to a process identification mapping rule, and allocating a workshop real-time data set to a quadtree index structure according to space coordinates to perform process state aggregation, and according to the viewport pixel mapping relation, performing weighted aggregation calculation on the process state aggregation matrix to obtain a pixel comprehensive display value, and based on the pixel comprehensive display value, performing layered rendering processing on the workshop multi-process parallel scene. According to the invention, the visual processing efficiency and the user interaction response speed of the digital twin system in the automobile industry are improved.

The invention provides an anomaly detection method and device for image data and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: carrying out the feature extraction of the image data, fusing the spectrum and spatial features to obtain a joint feature matrix, and inputting an anomaly detection model; the model adopts an alternating direction multiplier algorithm to solve a low-rank sparse decomposition problem, and an objective function comprises a data fidelity item, a regularization item and a waveband weight item; the regularization item comprises a low-rank constraint and a sparse constraint, and the wave band weight item acts on the low-rank constraint in a weighting form; updating a background low-rank tensor, an abnormal sparse tensor, a Lagrange multiplier, a sparse constraint weight, a wave band weight item and penalty parameters of an algorithm by adopting an iteration mode in a solving process; and repeating iteration until a preset termination condition is reached, calculating an abnormal score graph pixel by pixel based on the abnormal sparse tensor, and comparing to determine an abnormal target. The problem that an abnormal target is difficult to accurately recognize in a complex scene can be solved, and detection precision and efficiency are improved.

The invention relates to the technical field of image processing, in particular to an intelligent recognition and positioning system for battery support welding defects. Obtaining defect degrees and edge pixel points of the pixel points according to gradient features and neighborhood gray features of the pixel points in the welding image; obtaining change complexity according to the change characteristics of the local edges of the edge pixel points; edge sharpness is obtained according to the local gradient features and the gray level change features of the edge pixel points; obtaining local uniformity according to the local gray level distribution characteristics of the edge pixel points; obtaining a confidence coefficient according to the change complexity, the edge sharpness and the local uniformity; and obtaining an adjustment factor of the edge pixel point according to the confidence coefficient and the defect degree. The method comprises the following steps: adjusting a weighting coefficient in a high-lift filtering algorithm according to an adjustment factor to obtain a self-adaptive weighting coefficient; performing image enhancement and defect identification according to the adaptive weighting coefficient; and the accuracy of image enhancement and defect identification is improved.

The invention belongs to the technical field of image processing, and particularly relates to an oil and gas engineering supporting facility intelligent detection method based on machine vision, and the method comprises the steps: constructing a structure tensor matrix based on the gradient of pixel points, calculating a local linear structure response value according to two feature values of the structure tensor matrix, and calculating a local linear structure response value; according to the direction alignment degree of the feature vectors of the pixel points on the local path, the crack continuity of the pixel points is calculated, the morphological significance values of the pixel points are comprehensively obtained and used for adjusting basic contrast parameters, anisotropic diffusion processing is carried out on the to-be-detected image of the oil and gas facility based on the obtained self-adaptive contrast parameters, and the to-be-detected image of the oil and gas facility is obtained. An enhanced image is obtained through multiple times of iterative updating, and a defect detection result graph is obtained through edge detection. According to the method, the problem that false defects are easily misjudged as cracks in a traditional method is solved, and the detection accuracy and reliability are remarkably improved.

The invention discloses a pressure damage classification system based on image processing, which belongs to the technical field of image processing and comprises a pressure damage image acquisition module, a pressure damage image preprocessing module, a pressure damage classification model construction module and a pressure damage classification module. According to the method, a comprehensive noise characteristic value is obtained based on a pixel variance, an LBP value variance and a distance weight, an adaptive filtering weight is calculated, bilateral filtering and Gaussian filtering are fused for denoising, a detail enhancement factor is generated in combination with a local entropy value and a Sobel gradient amplitude, detail enhancement is carried out, the local contrast is enhanced, and the image quality is remarkably improved; according to the method, multi-scale features are extracted and fused, a space-channel attention mechanism is introduced to obtain a global weight, global enhanced fusion features and local enhanced fusion features are obtained, and comprehensive features are generated through fusion, so that the capability of identifying stress damage in different stages is remarkably improved.

The invention relates to the technical field of pattern classification, in particular to a pattern classification method and system based on a regional label, and the method comprises the following steps: extracting a local label threshold based on a pixel gray range, analyzing the regional difference of a pattern image, screening a change optimal group as a reference, analyzing a gradient direction and amplitude mutation, and screening layered breakpoints. And integrating the regional features, and judging category attribution to obtain a discriminant quantity. According to the method, detailed pixel relation analysis and regional structure feature extraction are executed step by step, label affiliation accurate adjustment is achieved based on local statistics and spatial attribute association synchronization, boundary anomaly is judged through gradient and label change multiple parameters, layering is carried out, nodes are automatically positioned according to attribute mutation, and category affiliation is decided by multi-dimensional attribute interaction. Layered and partitioned analysis of high-detail and complex patterns is realized, hierarchical attribution requirements under multiple scenes are adapted, the region classification accuracy is enhanced, and the response capability to the internal differential and hierarchical relationship of the pattern structure is improved.

The invention relates to the field of image processing, in particular to a circuit board microsection image defect detection method, which comprises the following steps: acquiring a circuit board microsection image, calculating local direction energy of each pixel point in the image in a preset scratch direction, and generating a direction energy diagram; calculating a direction continuity index of each pixel point by analyzing neighborhood energy distribution of each pixel point in the preset scratch direction; according to the direction continuity index, constructing a spatially adaptive scratch similarity for each pixel point; and on the basis of the scratch similarity, executing spatial adaptive anisotropic filtering processing on the circuit board microsection image so as to realize accurate detection of circuit board defects. According to the method, by intelligently recognizing the structural difference between the scratches and the defects, the background scratches are accurately removed, meanwhile, the contour integrity of the real defects is kept, and the accuracy and reliability of microsection defect detection are remarkably improved.

A system may detect a triggering condition by an imaging device. The system may capture, by the imaging device in response to the triggering condition, an image of a container. The system may perform, by a machine learning model, pixel quantification of the container. The system may determine, based on the pixel quantification, a status of the container.

The invention discloses a point cloud dyeing method, apparatus and device, and a readable storage medium. The method comprises the steps of obtaining a point cloud frame and a plurality of image frames; aiming at each space point of each camera device, generating an observation transformation matrix used for representing the projection movement condition of the space points on an image plane in the exposure process of the camera devices; generating a relative timestamp for indicating the imaging moment of the spatial point; based on the relative timestamp and the observation transformation matrix, generating a pixel coordinate of the spatial point projected to an image frame of the camera device, and based on the pixel coordinate, performing pixel color picking on the spatial point to obtain a pixel vector; establishing a corresponding relationship between each spatial point of different camera devices and each point cloud in the point cloud frame; and determining a color vector of the corresponding point cloud based on the pixel vector of each spatial point corresponding to the same point cloud. Visibly, according to the method, space-time alignment of the three-dimensional space points and the pixel coordinates on the image frame can be realized by constructing the observation transformation matrix and the relative timestamp, and the point cloud dyeing precision is improved.

The invention relates to the technical field of image filtering extraction, in particular to a vegetation growth path extraction method and system based on a remote sensing image, and the method comprises the steps: obtaining the multispectral remote sensing data of a vegetation region at each time point, obtaining a spectral information interference influence difference value according to the correlation between each pixel and the spectral data of pixels in a local region, and obtaining a spectral information interference influence difference value; the method comprises the following steps: clustering all pixels at the same time point, calculating a normalized vegetation index of each pixel according to filtering parameters when filtering multispectral data of the pixels in each cluster by a technology, and constructing a screened and adjusted characteristic value in a process of extracting a vegetation growth path by adopting an optimal path search algorithm, and adjusting the initial screening value of each pixel when the initial seed point is selected, further screening the initial seed point, and setting the moving direction of each pixel to extract a vegetation growth path. According to the invention, the accuracy of vegetation growth path extraction is improved.

The invention discloses a textile color difference detection method based on image data, and relates to the technical field of image processing and computer vision, and the method comprises the following steps: S1, collecting an image of a textile with a high reflection characteristic under a standard light source, constructing a light intensity prediction model, and carrying out the analysis based on pixel brightness distribution, identifying a brightness abrupt change region and a saturation critical region, and generating an image-level saturation risk probability graph; s2, performing multi-scale exposure area division on the image based on the image-level saturation risk probability graph, setting a target brightness range of each exposure control sub-area, and recording a global exposure parameter of the image acquisition equipment as an initial baseline of exposure scheduling; according to the method, pixel-level brightness convergence is realized through light intensity prediction and area exposure control, real color distribution is reconstructed in combination with color compensation, and a closed-loop process is constructed by introducing model self-correction, so that the detection precision and consistency of the micro chromatic aberration of the high-reflective fabric are improved, and the automation and industrial applicability are enhanced.

The invention discloses a cell occlusion relation estimation method and system based on a pixel-level focus evaluation curve. The method comprises the following steps: acquiring a multi-focal plane cell image sequence of the same view at different focusing positions; performing cell instance segmentation on the image sequence to obtain a two-dimensional cell instance mask; calculating the definition value of each pixel in the mask on each focal plane, and constructing a pixel-level focus response curve; determining a focus depth interval of each pixel according to the peak position of the focus response curve and a preset relative threshold value; and comparing focus depth intervals of different cell pixels in the cell overlapping region, and judging the up-and-down shielding relationship between the cells. According to the method, automatic analysis from the original microscopic data to the cell upper and lower layer relation is realized through pixel-level depth modeling and interval comparison, the spatial resolution and credibility of occlusion relation judgment are improved, the compatibility with a conventional microscopic imaging process is high, and deployment and popularization are easy.

The invention discloses a self-adaptive visual positioning method based on multi-scale feature interaction, belongs to the technical field of deep learning and computer vision, and aims to solve the problems of high calculation complexity, low local feature sensitivity and insufficient multi-scale feature fusion of a native Transform in a visual positioning method. The method comprises the following steps: S1, extracting local features of an input image through a CNN-based basic network; s2, extracting multi-scale features of the local features by using a multi-head adaptive adjustment Transform, carrying out interaction and dynamic weighted fusion on the multi-scale features, and outputting global integrated local features; s3, adaptively adjusting the dynamic fusion weight of the local features and the global integrated local features through a dynamic feature adjuster, and outputting a fused feature map; and S4, predicting the three-dimensional scene coordinate of the image and the one-dimensional uncertainty of each pixel based on the regression head, connecting the regression head in series twice to adaptively adjust the Transform and the dynamic feature adjuster, gradually compressing the feature channel, and finally outputting the six-degree-of-freedom pose of the camera.

In accordance with some embodiments, systems, methods and media for single-photon imaging with improved energy efficiency are provided. In some embodiments, the system comprises: an image sensor comprising pixels comprising detectors in an array, configured to: generate image data representing a scene, and comprising pixel values based on a signal generated by the respective detector during a first period of time; and a processor configured to: determine, for each pixel, an inhibition value based on the pixel value; determine, based on the inhibition value, that a pixel is to be inhibited during a second period of time; inhibit the pixel such that a pixel value associated with the pixel in second image data is consistent with the pixel being inhibited during a second period of time.

The invention relates to a modeling method, in particular to an unmanned aerial vehicle spectral image BRDF rapid acquisition and adaptive generalization modeling method. The method comprises the following steps: 1, determining a research area, obtaining multi-angle BRDF data of the research area, and obtaining illumination change data at the same time; 2, preprocessing the acquired multi-angle BRDF data in combination with illumination change data to obtain preprocessed multi-angle images, splicing the multi-angle images to generate a panorama of the research area, and acquiring a digital surface model graph at the same time; 3, carrying out pixel-by-pixel classification on the research area based on the panorama to obtain a ground feature category map; calculating the gradient and the slope direction of the research area pixel by pixel based on the digital surface model graph; 4, correcting a preset BRDF model by using the acquired illumination change data, gradient and slope direction; and 5, self-adaptive generalization modeling is carried out. According to the method, high-efficiency BRDF acquisition can be realized, and the influence of regional change, space environment and topographic factors on BRDF modeling precision can be eliminated.

The invention discloses a semantic segmentation method based on phase contour constraint, and belongs to the technical field of computer vision. The method comprises the following steps of: acquiring a two-dimensional texture image and a deformed stripe image of a scene to be detected in the same view field by using a calibrated camera-projector system; resolving the deformed fringe image through a multi-step phase shift and Gray code technology to obtain absolute phase distribution, and calculating a gradient field; according to a preset sudden change condition, extracting a physical edge contour reflecting the depth change of the object from the gradient field; and mapping the physical edge contour into the two-dimensional texture image based on a pixel coordinate corresponding relation to accurately define a semantic object region, endowing a corresponding category label, and finally generating a semantic label mask image. According to the method, the uncertainty of the visual texture edge is corrected by utilizing the physical phase information, the problem of edge extraction in a complex illumination scene is effectively solved, automatic generation of semantic segmentation data is realized, and the training precision and robustness of a semantic segmentation model are improved.

Provided is a pattern inspection system, including a scanning electron microscope (SEM) including an electron gun configured to generate a first electron beam and emit the generated first electron beam toward a first wafer, a detector configured to detect electrons emitted from the first wafer based on the first electron beam emitted toward the first wafer, and at least one processor configured to generate a first SEM image including a plurality of pixels based on the detected electrons, and determine, based on a period of a pattern extracted from the first SEM image, a pixel size of a second SEM image to be generated by the SEM with respect to a second wafer.

The invention provides a power transmission line external force damage hidden danger threat analysis method and system, and relates to the technical field of power transmission line detection. Power transmission channel images are collected through a tower camera, an external damage hidden danger detection and power line segmentation data set is constructed, images are inferred in parallel based on a double-branch detection algorithm, pixel coordinates of an external damage hidden danger target and a power line are obtained respectively, the threat level is judged in a space intersection detection mode, and an alarm is triggered only for a high-threat image. According to the method, high-precision hidden danger identification and accurate early warning based on the pixel-level spatial relationship are realized, and the intelligent identification and response capability of the external damage hidden danger of the power transmission line is improved.

A method and device for remote sensing image classification using quantum pixel matrix entanglement is provided. The method comprises: preprocessing an acquired raw remote sensing image to obtain image data containing multiband fusion; and calculating a pixel matrix entanglement coefficient μ and a Euclidean distance d between a cluster center and other pixel matrices based on a preprocessed remote sensing image, performing an iterative self-organizing classification on remote sensing image data based on the pixel matrix entanglement coefficient and a Euclidean distance threshold, and obtaining remote sensing image classification results.

This application provides a method for determining a wireless signal strength in an area, a computer-readable storage medium, and an electronic device. The method includes: obtaining building diagram information, wherein the building diagram information comprises a position of a target signal source, a target area, size information of one or more obstacles in the target area, type information of the one or more obstacles, and image calibration information; converting the building diagram information into a polar coordinate system; determining an attenuation amount of a target pixel based on the size information, the type information, the image calibration information, and a propagation path in the polar coordinate system, wherein the propagation path penetrates at least part of the one or more obstacles; and determining a signal strength of the target pixel based on a signal strength of the target signal source and the attenuation amount of the target pixel.

The invention discloses a cradle head dynamic control method and device, and the method comprises the steps: obtaining a two-dimensional pixel coordinate of a target object on a display plane, converting the two-dimensional pixel coordinate into a physical space coordinate through a coordinate conversion algorithm, and obtaining the pixel offset of the target object relative to the center of a cradle head; based on the pixel offset and the pixel density parameter, performing angle calculation by using a trigonometric function calculation module to obtain rotation angle parameters required by the horizontal axis and the vertical axis of the holder; pWM control signal conversion is carried out on the rotation angle parameter, a corresponding number of pulse waveforms are generated through a stepping motor driving module, and a holder double-shaft motor is controlled to rotate; and based on the deviation value between the current position of the holder and the target angle, real-time error correction is performed by adopting a PID control algorithm, so that the holder tracks the target object in real time and keeps pointing to the position of the target object. Through the solid geometry correction technology, the problems of precise positioning and dynamic tracking of a traditional pan-tilt control system in a complex scene are solved, and the precision and stability of pan-tilt control are improved.

A method of predicting a lifespan of a display device includes preparing a test panel including a test light emitting element including a first and second test light emitting layers, measuring a luminance of the first and second color of the test panel to acquire first and second deterioration data of the first and second color, calculating a lifespan of a pixel based on the first and second deterioration data, and a first and second color component ratio of first emission light for a thickness of a wavelength conversion layer. The pixel includes a light emitting element emitting the first emission light and mixed light of the first and second color, and the wavelength conversion layer, and a light emitting layer of the light emitting element includes a first light emitting layer emitting light of the first color, and a second light emitting layer emitting light of the second color.

The present disclosure provides systems, devices, apparatuses, and methods, including computer programs encoded on a storage medium, for panel aging condition recording strategies for OLED aging resistance. A display processor may detect that a value of a set of pixels displayed on a display panel remains static for a period of time and the value of the set of pixels exceeds a threshold for the period of time. The display processor may perform anti-aging pixel recording on the set of pixels based on the detection. The display processor may adjust a value of the set of pixels based on the anti-aging pixel record.