546 results about "Edge maps" patented technology

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention belongs to the technical field of computer vision, and relates to a dynamic scene online calibration method and system for three-dimensional target detection. The method comprises the following steps: firstly, acquiring a 3D point cloud around a vehicle, an initial external parameter and a synchronous image, and processing the 3D point cloud, the initial external parameter and the synchronous image by a bimodal static mask generation module to obtain a static region probability graph; calculating a multi-scale residual error of the image and the LiDAR edge image in a static region through a static region fusion module; a historical time sequence modeling module is used to generate time sequence fusion features; performing external parameter correction through a coarse-fine multi-stage external parameter correction module; and finally, according to the predicted external parameters, performing three-dimensional target detection through an image-point cloud bidirectional enhancement module in combination with Transform. Full-scene self-adaptive calibration is achieved, the invalid calibration rate is reduced by 67%, permanent deformation and temporary interference of the sensor can be effectively distinguished, the diagnosis accuracy rate reaches 92%, the false alarm rate is lower than 0.5%, and the three-dimensional target detection performance is improved.

The invention discloses a welding spot defect detection method, electronic equipment and a storage medium. The mode relates to the field of image detection, and the method comprises the following steps: obtaining a to-be-detected image of a to-be-detected welding spot; edge detection is carried out on the to-be-detected image, a welding spot edge image of the to-be-detected welding spot is obtained, and the welding spot edge image is used for representing contour features of the to-be-detected welding spot in the image space; parameter extraction is conducted on the welding spot edge image, welding spot shape information of the to-be-detected welding spot is obtained, and the welding spot shape information is used for representing shape features of the to-be-detected welding spot in a parameter space; welding spot defect detection is conducted on the to-be-detected welding spot based on the welding spot shape information, a defect detection result is obtained, and the defect detection result is used for representing whether the to-be-detected welding spot has welding defects or not. According to the invention, the technical problem of low efficiency of detecting the welding spot defect in the prior art is solved.

The invention discloses an impregnated paper surface uniformity detection method based on texture image analysis, and particularly relates to the field of image enhancement and texture analysis, and the method comprises the steps: obtaining an impregnated paper image, constructing an edge image based on a gray difference value, extracting an image region with an edge value lower than a first threshold value in the edge image, and defining the image region as a fuzzy region; and edge value difference calculation between adjacent pixels is carried out on the fuzzy region to form a difference chart, and a pixel region with an edge change value greater than a second threshold value is extracted from the difference chart and is defined as a fracture region. According to the scheme, the weak boundary between the adhesive film and the fiber is extracted by recognizing the fuzzy region with the low edge value and tracking the direction path of the fuzzy region, the problem that the low-contrast edge cannot be recognized through a traditional method is solved, and the detection precision of the non-uniform region is improved.

The invention discloses an image edge cutting method and device based on boundary detection, equipment and a storage medium, and relates to the technical field of image boundary detection, and the method comprises the steps: carrying out the preprocessing of an original image, and obtaining a target image; determining a scene type of the target image; based on an edge detection strategy corresponding to the scene type, performing edge detection on the target image to obtain an edge image; performing straight line detection on the edge image to obtain a plurality of candidate straight line segments; and based on a boundary positioning rule, determining a target boundary line segment from the plurality of candidate straight line segments, so as to carry out edge cutting on the original image according to the target boundary line segment. According to the invention, a more reliable image edge cutting effect can be generated.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an image segmentation method, device, equipment and medium based on multi-channel edge superposition. Only the mask decoder and the expansion module are updated; obtaining a target image and prompt information, and obtaining an initial mask image; generating a multi-channel edge image set, and superposing the multi-channel edge image set with the target image and the initial mask image channel to form a multi-channel input image; and processing by a gating convolution unit, a channel compression convolution layer and a multi-stage attention decoder to generate an enhanced mask image, and outputting a target segmentation result. According to the invention, the training overhead is reduced through the frozen image encoder and the prompt encoder, and the capability of capturing boundaries and details is enhanced by using the extension module and the attention decoder, so that the definition and accuracy of a segmentation result are improved.

The embodiment of the invention provides an edge detection method and device based on gradient weighted fusion and an adaptive threshold, and is applied to the field of computer vision and digital image processing. The method comprises the steps of firstly preprocessing an input image, then extracting two groups of gradient magnitude diagrams and directional diagrams through an adaptive morphological operator and a traditional difference operator, and constructing a weighted fusion function according to the gradient direction consistency of each pixel point to obtain a fused gradient magnitude diagram; and adaptively determining a high threshold and a low threshold based on an information entropy maximization principle, executing an improved Canny process by combining the fused gradient magnitude diagram and the second gradient directional diagram to obtain an initial edge diagram, and outputting a final edge detection result after dynamic structure element optimization. In this way, the defects that in a traditional edge detection method, gradient information extraction is not precise, threshold selection lacks adaptability, and an edge result is fractured can be overcome, more robust and more accurate image edge detection is achieved, and the reliability of an edge detection algorithm in a complex image scene is improved.

The invention provides a multi-modal alignment and cross-scale structure attention-based remote sensing image building extraction method, which comprises the following steps of: performing radiation correction and geometric correction on an original hyperspectral satellite image, and performing voxelization, normalization and registration with a hyperspectral corrected image on LiDAR point cloud data to respectively obtain the hyperspectral corrected image and LiDAR corrected data; deep features of the two types of correction data are extracted through a special encoder, and after dimension unification and channel attention screening, fusion features are obtained through fusion of a weighting and gating mechanism; multi-scale features are extracted from the fusion features through multi-receptive field convolution branch, and cross-scale enhancement features are obtained by combining cross attention and geometric modeling; a prediction edge graph is generated based on cross-scale enhancement features, high-resolution guide features are combined to compensate tiny building information, and the extraction precision is improved through edge loss optimization; and carrying out denoising, edge repairing and adhesion separation on the optimized result to obtain a final building extraction result.

The invention belongs to the field of machine vision, and relates to a novel circle detection method in a complex environment. The method comprises the following steps: firstly, preprocessing an input color image, converting the color image into a grayscale image, then performing Gaussian blur processing, and applying a Canny edge detection algorithm to obtain an edge image; then, a findContours function of OpenCV is used for detecting a contour in the edge image, and traversal processing is carried out on the contour; secondly, circle detection is conducted on each contour through a findcircle () function, and circle fitting is conducted through a three-point circle determination method or a least square circle fitting method; performing accuracy verification on the fitted circle; and finally outputting the number and parameters of the identified circles, such as circle center coordinates and radiuses. The method is low in operand, can rapidly and accurately detect a circle in a complex environment with a weak light source and noise, does not need to occupy high computing resources, and has high detection speed and precision.

The invention relates to the technical field of image processing, in particular to a printing scraper damage online detection method based on machine vision. The method comprises the steps of collecting a surface image of a scraper, performing edge detection on the surface image to obtain an edge image, and obtaining a connected domain and a to-be-detected area corresponding to the connected domain in the edge image; analyzing the connected domains and the to-be-detected areas corresponding to the connected domains to obtain edge continuity characteristic values and shape regularity characteristic values of the to-be-detected areas, and screening all the to-be-detected areas to obtain candidate areas; determining main radiation directions by taking the central point of one candidate area as an original point, and acquiring a pixel point sequence corresponding to each main radiation direction; based on the pixel point sequence corresponding to each main radiation direction of one candidate area, obtaining a texture periodicity quantitative index of the candidate area, and finally screening all the candidate areas to obtain an abrasion and corrosion area. According to the invention, the damage of the printing scraper can be accurately detected.

The application discloses a kind of detection and defense method of counterpatch, respectively based on abnormal positioning and edge detection.The detection method of counterpatch based on abnormal positioning utilizes clean image to train the generator-adversary network of encoder-decoder structure;The output image is obtained by inputting the image to be detected into the generator-adversary network, and the absolute error is obtained by subtracting and taking absolute value;The image region whose absolute error is greater than error threshold is the region where counterpatch is located;The detection method of counterpatch based on edge detection converts the image to be detected into gray scale image and carries out edge detection, and obtains edge image;The edge lines in edge image are connected into a closed area one by one, and the closed area whose area is less than the area of counterpatch region is the region where counterpatch is located.The application can detect counterpatch based on the two schemes of abnormal positioning and edge detection respectively, and blacken the region or use image restoration algorithm to restore the region to defend counterpatch.

The invention discloses a discharge channel extraction method and system based on edge detection and texture feature fusion, and belongs to the technical field of power equipment fault diagnosis and digital image processing, and the method comprises the steps: obtaining an ultraviolet weak light image of power equipment, and carrying out the preprocessing; multi-scale edge detection is executed based on the preprocessed image, edge information is fused through a multi-scale voting mechanism, and a candidate edge graph is generated by combining direction consistency connection fracture edges; extracting texture features of the pre-processed image, wherein the texture features comprise a rotation invariant local binary pattern uniformity feature and a multi-direction gray level co-occurrence matrix feature; according to a dynamic distribution weight coefficient of a discharge type, carrying out weighted fusion on the edge intensity of the candidate edge graph and the texture features, and generating a discharge channel confidence score graph; and carrying out binarization segmentation and contour optimization processing on the confidence score graph of the discharge channel, and extracting morphology quantization parameters of the discharge channel.

The invention discloses an image fusion method based on multi-dimensional complementary features and multi-scale detail enhancement, and the method comprises the steps: calculating an edge image of a source image, and splicing the edge image with the source image in a channel dimension; enhancing feature textures and edge details by a high-frequency detail enhancement module; generating three feature maps with different scales step by step through continuous down-sampling operation; from the deepest scale, the infrared and visible light features of the hierarchy are fused through a parallel three-branch fusion module; the fused features are used for reconstructing a low-resolution fusion result, and the low-resolution fusion result is transmitted to an upper layer through up-sampling operation; and in the scale of the upper layer, the steps are repeated until a final full-resolution fusion image is generated. A parallel multi-dimensional complementary fusion network PMCFusion is set, cooperative perception and efficient integration of spatial features, channel correlation and frequency domain information are achieved through a parallel three-branch fusion module PTFM, and through cross-dimensional attention interaction, the PMCFusion can selectively enhance multi-dimensional features and achieve deep complementation.

The invention discloses an image processing method and device, a storage medium, equipment and a program product, which are applied to a style migration scene. The method comprises the following steps: obtaining a cue word, an original image containing a target subject and a background image with a target background style, wherein the cue word is used for prompting to generate an image containing the target subject and with the target background style; splitting the original image to obtain a target main body area image, a mask image and an edge image; carrying out splicing processing on the target main body area graph, the mask graph and the edge graph to obtain a spliced image; according to the spliced image, the cue word and the background image, style migration processing is carried out, a target image matched with the cue word is obtained, and the target image comprises a target body and has a target background style. Through double guidance of the cue word and the background image, the original image is subjected to style migration to generate the high-quality target image, and the target main body in the target image and the target background style have a high fusion degree.

The invention relates to the technical field of images, in particular to an image vectorization method and system based on deep learning and edge detection, and the method comprises the steps: obtaining image data of a household appliance module, and carrying out the preprocessing of the obtained image data; taking the preprocessed image data as input to construct an edge detection model, performing optimization processing on an edge probability graph output by the edge detection model, performing edge detection on the preprocessed image data in sequence to generate multiple groups of edge graphs, and performing average processing on the multiple groups of edge graphs to obtain a comprehensive edge image; the optimized marginal probability graph and the comprehensive marginal image serve as input, marginal information is converted into a vector graph, a DXF file is obtained, the optimized marginal probability graph and the comprehensive marginal image serve as input, vectorization conversion is conducted through the Potrace algorithm, optimization processing on the marginal detection result in the early stage is combined, and the edge detection efficiency is improved. Vector graphs with higher precision and less noise can be generated.

The invention relates to the technical field of product design, in particular to a household appliance appearance defect optimization product design method based on image discrimination, which comprises the following steps: acquiring a household appliance appearance image, calculating a gradient direction, constructing a distribution matrix, analyzing consistency to generate a defect edge image, extracting high-frequency feature scores to identify defect areas, and clustering point cloud to output defect clusters. According to the method, by calculating the gradient direction numerical value of each pixel point and constructing the gradient direction distribution matrix, the processing capacity of the directivity difference of the household appliance surface texture is effectively improved, the defect point cloud is accurately divided through the density-based noise application spatial clustering algorithm, the defect point cloud is accurately divided, and the defect point cloud is accurately divided. According to the method, the space positioning accuracy of the defect area is improved, the precision of optimization design is ensured, curved surface reconstruction is performed in combination with the least square method, the appearance design of the household appliance is accurately corrected, and the production efficiency and the design quality are optimized.

The invention relates to the technical field of traditional Chinese medicine recognition, in particular to a traditional Chinese medicine recognition system based on an image edge detection algorithm, which can effectively extract edge information of a traditional Chinese medicine image, reduce noise interference and ensure that key information in the image is accurately reserved, and has excellent image edge detection capability. And the method also has strong feature extraction and classification identification capabilities. In the feature extraction module, the system extracts multi-dimensional information such as shape features, edge contours, texture features and color distribution by performing morphological analysis on edge images.

The utility model discloses a wafer contour measuring instrument, and relates to the field of wafer production equipment. The wafer contour measuring instrument comprises a machine body, wherein a workbench is arranged in the machine body; the wafer contour measuring instrument further comprises a loading and unloading module which is arranged on the machine body and used for loading and unloading a wafer, and a bearing module, an edge searching module, an adjusting module, an original point detection module and a measuring module which are arranged on the workbench. The carrying module is used for transferring the wafer between the loading and unloading module and the bearing module; the bearing module is used for driving the wafer to move; the edge searching module is used for acquiring edge image information of the wafer; the adjusting module is used for adjusting the position of the wafer, so that the center of the wafer is aligned with the rotating center of the bearing module; the bearing module is also used for driving the wafer to move to the original point of the wafer to be aligned with the detection point of the original point detection module; the measuring module is used for measuring the contour of the wafer. The wafer contour measuring instrument provided by the utility model is higher in measuring efficiency and better in measuring precision.

The invention relates to the field of textile defect detection, in particular to a textile defect detection method which comprises the following steps: S1, acquiring first image data of a textile; s2, extracting second image data containing printing and dyeing patterns from the first image data; s3, printing and dyeing image data and edge image data are constructed according to the second image data; s4, calculating the stretching degree value of the textile in each stretching direction according to the edge image data; according to the method, when the stretching degree of the textile in each direction is calculated, due to the fact that the texture of the textile instead of a foreground pattern is analyzed, the method is not sensitive to interference factors such as color changes, pattern content and slight stains, and compared with a common detection method, accurate stretching percentage values in all directions are provided, and the detection accuracy is improved. And simple qualification or disqualification judgment is not carried out, so that the calculation precision is improved.

The invention relates to the technical field of intelligent recognition, in particular to a distributed edge calculation face recognition method and system for non-inductive passage, and the method comprises the steps: capturing a video stream through edge image collection equipment disposed at a passage entrance, and extracting a face image; and carrying out localized preprocessing, feature extraction and quick comparison with a local cache feature library by the edge computing node. If local matching succeeds, passing is controlled immediately; if the matching fails, the features are uploaded to a central cloud server, enhanced processing and global library comparison are performed by using the stronger computing capability of the central cloud server, and a result is issued and executed, so that the low delay of edge computing and the strong capability of cloud computing are effectively fused, and both the recognition efficiency and the system reliability are considered; and meanwhile, links such as encrypted transmission, dynamic updating, abnormal offline processing and detailed log recording are covered, the security, adaptability and maintainability of the system are remarkably improved while efficient non-inductive traffic is realized, and the method is suitable for intelligent traffic management in a large-scale and high-concurrency scene.

The present application provides a corner point detection method and apparatus. The corner point detection method comprises the following steps: receiving a picture to be processed, generating a corner point set, performing edge detection on said picture and obtaining an edge map, and extracting M curves from the edge map; for each curve, performing the following processing: filling gaps along the curve, determining a plurality of T-junction points on the curve, adding the plurality of T-junction points into a corner point set S, smoothing the curve, acquiring the curvature values of all points in the curve, identifying a plurality of corner points on the basis of the curvature values of all the points, and adding the plurality of corner points into the corner point set, wherein the corner points in the corner point set are corner points detected from said picture. In this way, a corner point detection method is implemented.

The invention discloses a diffusion model image completion method based on boundary preserving perception, and belongs to the technical field of artificial intelligence and image generation. The method comprises the following steps: firstly, preprocessing an input image and a corresponding defect mask, and extracting gradient attenuation boundary features and a structure edge graph; a boundary sensing convolution module is introduced into the completion model and is used for boundary information modeling of a coding end; meanwhile, a gating mechanism is adopted in U-Net jump connection, the weight of a gradient attenuation mask is combined, and the boundary information transmission intensity is dynamically adjusted; furthermore, in the diffusion generation process, a region self-adaptive scheduling strategy is adopted, only high-step diffusion iteration is carried out on a defect region and a boundary neighborhood, and low-step or freezing operation is carried out on a non-defect region, so that balance between boundary consistency and calculation efficiency is realized. Experimental results show that the method is superior to an existing diffusion model image completion method in the aspects of structure retentivity, edge continuity and visual quality, and has wide application prospects.

The invention relates to the technical field of image processing, and provides a pipe fitting stretching surface feature extraction method, which comprises the following steps: obtaining a surface image of a pipe fitting stretching surface, and obtaining an initial edge image of the pipe fitting stretching surface through edge detection; quantifying the surface concavity and convexity of the stretching surface of the pipe fitting based on the pixel gradient difference expression of the stretching surface of the pipe fitting; combining the spectrogram width of the initial edge image and the amplitude fluctuation condition of the high-frequency component to obtain the surface roughness influence degree of the stretching surface of the pipe fitting; the glossiness difference of all pixel points of the pipe fitting stretching surface is analyzed, and the surface flatness degree of the pipe fitting stretching surface is obtained; obtaining the surface roughness scattering degree of the stretching surface of the pipe fitting; edge continuity of edge detection is determined, and final optimization parameters of edge detection are obtained; and obtaining a final edge image, and carrying out feature extraction on the pipe fitting stretching surface based on the edge expression in the final edge image. The invention aims to solve the problem of feature loss caused by incapability of accurate extraction due to surface quality interference in a pipe fitting stretching surface feature extraction process.

The invention relates to the technical field of building decorative plate production detection, and discloses a building decorative plate production quality rapid detection method based on machine vision. The method comprises the following steps: firstly, acquiring a surface image, an edge image and a texture feature sequence of each visual detection unit in each detection period; obtaining a feature deviation index of an abnormal point based on a correlation deviation condition of the abnormal point in the texture feature sequence, and further obtaining an average deviation index and a quality fluctuation degree at a quality fluctuation collection moment, thereby determining a surface quality coefficient; obtaining an edge quality coefficient based on the surface and edge images; and finally, according to the surface and edge quality coefficients, obtaining a detection parameter adjustment value which is used for correcting a detection parameter and a threshold value for template matching defect identification in the next period. According to the method, the dynamic adjustment of the detection parameters is realized, and the accuracy of defect identification is improved.

The invention discloses a high-resolution video generation method and system based on multi-modal semantic guidance, and belongs to the technical field of video generation. According to the method, firstly, a low-resolution video is generated according to a text prompt, an edge map, a depth map and optical flow features of the low-resolution video are extracted through a space-time perception module, the features are combined with the text prompt, and a saliency region mask is generated after saliency weighting. And then the edge map, the depth map, the optical flow features and the text prompt are input into a DiT model. In each layer of Transform of DiT, after input is converted into corresponding embedded representation, structural feature embedding and text embedding are aligned in a space-time dimension, global semantic association is established through a cross attention mechanism, text embedding is processed by MLP to form conditional constraints, and sparse constraints and mask guidance are applied to an attention matrix. And finally, after multi-layer Transform processing, the model outputs a high-resolution video. In the whole process, the generation from the text to the high-resolution video is realized, and the details of the saliency region are emphatically protected.

The present application discloses an edge detection method and apparatus for a battery insulation part, and a battery production line. In the edge detection method for a battery insulation part, the battery insulation part is used for wrapping at least the side face of a battery cell of a battery. The method comprises: irradiating an edge to be detected of the insulation part by means of at least two light source assemblies; imaging said edge by means of an imaging assembly which is arranged to face said edge, so as to obtain an edge image; and identifying the position of said edge from the edge image. The edge detection method in the present application can solve the problem of performing standardized, stable, and reliable edge detection on the battery insulation part, and by making said edge of the battery insulation part more clearly visible in the edge image, improve the accuracy of extracting said edge from the edge image.

A method includes selecting a reference frame and a non-reference frame from a plurality of image frames. The method also includes generating a reference edge map identifying edges in the reference frame and a non-reference edge map identifying edges in the non-reference frame. The method further includes generating a moving edge map based on one or more movements between one or more of the edges of the reference edge map and one or more of the edges of the non-reference edge map. The method also includes generating a blend map based on the reference and non-reference frames. The method further includes modifying the blend map based on one or more indications of movement of corresponding pixels in the moving edge map to generate a modified blend map. In addition, the method includes blending the reference and non-reference frames based on the modified blend map to generate an output image.

The invention relates to the technical field of precision machining, and discloses an on-machine intelligent detection method and system for geometric parameters and abrasion states of a cutter, and the detection method comprises the following steps: irradiating the cutter through a light source, and collecting a cutter image; the cutter image collected under irradiation is machined; on the basis of the machined cutter image, obtaining a measured value of abrasion, extracting an edge image, and on the basis of the edge image, identifying the type of the cutter and extracting geometric parameters of the cutter; based on the tool type and the tool geometric parameters, determining a wear damage threshold value in tool wear detection; and comparing the measured value of the abrasion with the threshold value of the abrasion so as to judge whether the parameters of the cutter are out of tolerance or whether the abrasion exceeds the standard. The problems that during high-strength and high-hardness material machining, cutter types are mismatched, abrasion damage is difficult to recognize in time, manual missing detection exists in an existing detection technology, potential safety hazards exist, and reliability is low are solved.

The invention belongs to the technical field of image recognition and processing, and discloses an industrial drawing table data extraction method and system and terminal equipment, and the method comprises the steps: carrying out the preprocessing of an initial industrial drawing image; reprocessing the first industrial drawing image by using an edge detection algorithm and morphological operation, and then detecting to obtain a table edge image; identifying table cells in the table edge image to obtain a table structure model; identifying contents in all cells in the table edge image to obtain target identification contents; according to the structure information of the cells and the business logic rule, whether the target identification content is wrong or not is verified, the wrong target identification content is repaired, the target table data is obtained, and efficient and accurate extraction of the industrial drawing table data is achieved.

The invention relates to the field of industrial internet, in particular to an industrial internet quality detection system based on edge cloud collaboration, which is characterized in that an edge acquisition module acquires image data of a production line according to a preset time period, and an edge image processing module extracts contrast features and surface texture features in the image data; a defect area contour is identified based on a standard product contour, an edge image analysis module calculates a surface anomaly index and a defect anomaly index, and a comprehensive defect score is obtained through weighting; the edge uploading module sets labels for the image data according to the comprehensive defect scores, sampling sending under high-quality labels and complete sending under low-quality labels are achieved, and the cloud analysis module conducts high-precision defect evaluation on the received image data and conducts fault early warning through time sequence analysis. The method improves the detection efficiency, reduces the network bandwidth pressure through a hierarchical uploading mechanism, and achieves the judgment of the quality degradation trend through the high-precision evaluation and time sequence analysis capabilities of the cloud.