703 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 provides a PCBA circuit board defect detection method, apparatus and device, and a storage medium. The method comprises the steps of obtaining three-dimensional point cloud data of the surface of a circuit board by projecting multispectral composite structured light; performing feature fusion on the multi-modal image of the circuit board according to the three-dimensional point cloud data to generate a fused multi-channel feature map; performing non-rigid registration on the acoustic impedance distribution data and the three-dimensional point cloud data to generate an acousto-optic fusion defect probability distribution diagram; performing multi-scale edge detection on the fused multi-channel feature map to generate an optimized binary edge map; and performing scattering transformation on the optimized binary edge graph, and determining the defect type of the circuit board according to the acousto-optic fusion defect probability distribution graph. Through implementation of the scheme, non-rigid registration is performed by using the acoustic impedance distribution data and the three-dimensional point cloud data, the acousto-optic fusion defect probability distribution diagram is generated, the surface three-dimensional information and the internal acoustic impedance data are combined, internal hidden defects are effectively identified, and comprehensive detection of surface and internal defects is realized.

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 provides a multi-scale polarization data fusion method based on a 4K polarization imaging detector, and relates to the technical field of image processing, and the method comprises the steps: carrying out the adaptive edge detection of four original polarization images, generating a comprehensive edge image, and carrying out the definition scoring; performing color calibration on each original polarization image based on the ambient light intensity and the color temperature, and outputting a polarization image after color calibration and a color deviation score; combining definition scores, color deviation scores and global brightness information entropies of the four polarization images after color calibration to construct a multi-objective optimization function, and solving the multi-objective optimization function in a multi-exposure parameter space to obtain an optimal exposure parameter combination; applying the optimal exposure parameter combination to image capture of the next period, and calculating global quality reward values of four newly collected polarization images; when the global quality reward value meets a preset condition, wavelet decomposition and frequency division multi-scale fusion are carried out on the polarization images after color correction, a fused image is generated, and the quality of the polarization images is effectively improved.

The invention discloses a traditional Chinese medicinal material condition feature extraction and classification processing method and system, and the method comprises the following operation steps: collecting a medicinal material image of gastrodia elata, and carrying out the preprocessing of the medicinal material image of gastrodia elata to obtain a to-be-detected medicinal material image; performing edge detection on the to-be-detected medicinal material image to extract contour information to obtain a to-be-detected edge image; and extracting multi-scale mildew features of the to-be-detected edge image, and identifying abnormal defects of the rhizoma gastrodiae medicinal material image through the multi-scale mildew features. According to the invention, multiple features are extracted from the abnormal region of the to-be-detected edge image, so that mildew can be judged, and missing detection can be prevented according to recognition of holes.

The invention relates to the field of image processing, in particular to an automobile part processing burr detection method, which comprises the following steps of: positioning an automobile part, acquiring an edge image to define an ROI (Region of Interest), performing sub-pixel extraction on an edge in the ROI, and performing multi-scale decomposition by applying discrete wavelet transform to obtain detail coefficients. The fractal dimension of the edge local contour is calculated, the expected detail energy of the edge local contour under the corresponding wavelet scale is deduced, then the adaptive threshold adjustment amount is obtained through calculation, the final local adaptive threshold is generated in combination with the basic threshold, and the threshold is used for judging the actual detail coefficient to accurately position burrs. And then carrying out feature quantification on the positioned burrs. And finally, comparing the quantized burr characteristics with a preset quality standard, judging whether the part is qualified or not, and outputting a result. According to the method, the wavelet threshold is adaptively adjusted by introducing fractal features, so that the accuracy and robustness of burr detection under different edge conditions are 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 belongs to the technical field of image data processing, and particularly relates to a quality detection method and system for metal pipe fitting forming, and the method comprises the steps: obtaining a plurality of groups of matched gap regions of a port edge image according to the gray scale difference of the neighborhood of each gap of the port edge image in a corresponding metal pipe fitting image; obtaining a suspected defect area of the port edge image according to curvature change of edge pixel points of the port edge image; acquiring similar arc segments of each suspected defect area according to the change of the distance from the arc segment on the ellipse of the metal pipe edge image to the ellipse center; obtaining the abnormal degree of each suspected defect region according to the deformation difference between the suspected defect region and the similar arc section and the gray difference between the suspected defect region and the corresponding suspected defect region of the same group of matched gap regions; and screening to obtain a final defect area. According to the invention, the accuracy of quality forming detection of the metal pipe fitting is improved.

The invention provides a disaster site satellite communication blind area image acquisition and transmission method of a low-altitude unmanned aerial vehicle relay. The method comprises the following steps: acquiring disaster area information and executing adaptive multi-modal image acquisition; establishing a low-altitude unmanned aerial vehicle relay network; performing feature extraction and semantic analysis on the marked image data set, generating non-uniform image fragments and calculating a transmission priority; and monitoring a communication environment state, and dynamically adjusting a transmission strategy through a dual-cycle feedback mechanism. According to the method, semantic integrity is ensured, transmission window perception is realized, parameter adjustment is stabilized through a double-loop feedback mechanism, and the problem of efficient transmission of the satellite communication blind area edge image in a disaster site 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.

A method for evaluating edge quality in an image includes receiving an image from a camera of a vehicle, detecting an edge in the image captured by the camera, fitting a polynomial curve to the edge in the image captured by the camera, executing a numerical optimizer to determine a minimum distance from each pixel in the image of the edge to the polynomial curve, determining a modulation transfer function (MTF) value using the minimum distance, determining whether the MTF value is greater than predetermined threshold, and providing an alert in response to determining that the MTF value is not greater than the predetermined threshold.

The invention discloses a broken rock granularity identification method based on machine vision. The method comprises the following steps: S1, obtaining a broken rock pile image in a quarry; s2, based on the lightweight edge segmentation model, combining a deformable convolution module fusing attention and an attention residual shrinkage unit, and replacing a maximum pooling layer with a cavity convolution layer to obtain an improved lightweight edge segmentation model; s3, segmenting the macadam pile image, and distinguishing macadam edges and backgrounds in the macadam pile image to obtain a macadam edge graph; s4, carrying out edge refinement on the broken stone edge graph by adopting a table look-up method, and carrying out area filtering by combining an area filter so as to refine edges and eliminate interference edges; and S5, carrying out macadam edge contour extraction on the macadam edge graph, calculating the area and perimeter of the macadam, and finally obtaining the granularity distribution condition of the macadam. According to the detection method, the calculation amount is remarkably reduced while high accuracy is kept.

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 a system and method for detecting the angle of a wafer positioning groove, and the system comprises a wafer box, the side surface of which is provided with a side surface opening used for the incoming and outgoing of a wafer; the wafer slot is positioned in the wafer box and is used for accommodating a wafer; the plurality of infrared scanning devices are coupled to the inner wall of the wafer box so as to scan and transmit edge surface information of the wafer; and the processing module is used for generating a wafer edge image based on the edge surface information, responding to a wafer positioning groove contained in the wafer edge image, and determining the angle of the wafer positioning groove. By adopting the technical scheme, the problems of time waste and low accuracy of obtaining the angle of the positioning groove before the wafer enters the machine table can be solved.

The invention provides an ear-nose-throat department intelligent diagnosis and monitoring system based on multi-modal data fusion, and relates to the technical field of multi-modal data fusion. Multi-modal monitoring data is acquired, and an endoscope monitoring image of a patient is extracted; performing edge monitoring on the endoscope monitoring image to obtain an endoscope monitoring edge image, generating a secretion disturbance mask according to the endoscope monitoring edge image, and determining an endoscope monitoring restoration image according to the secretion disturbance mask and the endoscope monitoring image; determining a pixel decision window of each pixel point in the endoscope monitoring restored image, and performing information decision based on the corresponding pixel decision window to obtain an endoscope monitoring decision image; and extracting monitoring text data, and performing feature fusion on the monitoring text data and the endoscope monitoring decision image to generate a fusion association map. According to the method, the image quality of the endoscope monitoring image can be improved, and the correlation map of the patient is generated in combination with the monitoring text, so that knowledge fusion in ear-nose-throat monitoring is realized.

The invention relates to the technical field of image data processing, in particular to an endoscope image intelligent optimization method and system. The method comprises the following steps: acquiring a grayscale image of an endoscope image of a stomach, acquiring an edge image of the grayscale image, and performing region growth on pixel points with gradient values greater than a preset gradient in the edge image to obtain a plurality of first connected domains; according to the area of the minimum enclosing rectangle of the first connected domain and the number of edge pixel points, determining a suspected lesion area in the grayscale image, and taking the minimum grayscale value in the suspected lesion area as an initial segmentation threshold value; and gradually increasing the segmentation threshold value from the initial segmentation threshold value, segmenting the suspected lesion area to obtain a plurality of binary images, and determining a target lesion area according to the difference degree of newly added pixel points between the adjacent binary images in discreteness characteristics. By means of the technical scheme, the accurate focus area in the endoscope image of the stomach can be obtained.

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 discloses an intelligent PET base cloth cutting control system, and relates to the technical field of material cutting. By means of dynamic signal collection, frequency decoupling, edge image recognition and self-adaptive control, full-process intelligent regulation and control of the PET base cloth cutting process are achieved, vibration marks are accurately restrained, the edge quality is optimized, the cutting precision and the product consistency are remarkably improved, and the method is suitable for high-end application such as optics and electricity and has wide application prospects.

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 invention relates to a wafer pose recognition system and an image acquisition device and an image acquisition method thereof, the wafer pose recognition system comprises a support structure for supporting a wafer to be recognized and the image acquisition device arranged above the wafer to be recognized, and an image sensing module is arranged in the image acquisition device. A fisheye lens group or a negative lens is adopted to enlarge the visual field width of an image sensor, so that the image sensing module can obtain complete edge image information of a wafer in one-time imaging, the positioning efficiency is improved, an annular first light shielding plate and a circular second light shielding plate are arranged on an optical path of the lens to form an annular visual field in a matched mode, and the positioning precision is improved. The edge blurred part image and the middle too bright part image are physically excluded, the imaging quality in the annular view is improved, the clear and complete wafer positioning edge image can be obtained, the contour recognition precision during image recognition is improved, and the positioning accuracy is improved.

The invention discloses a terahertz image registration method fusing morphological characteristics. The terahertz image registration method comprises the following steps: filtering and denoising a terahertz image; performing adaptive threshold segmentation processing on the reference image and the to-be-registered image, and respectively generating three types of images, namely a grayscale image, a binary image and an edge image; extracting texture features, geometric features and contour features from the generated grayscale image, the binary image and the edge image, and carrying out integration and feature description on the extracted feature points; mismatching points are removed through bidirectional feature point matching, registration parameters are calculated based on correct matching points, and image registration and verification are completed. According to the method, the number of extracted feature points in terahertz can be increased, multiple features are fused for registration, the registration precision is improved, image registration can be achieved in similar images, the practicability is high, and the application range is wide.

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 method for reconstructing a three-dimensional edge of a multi-view image. The method comprises the following steps: step 1, carrying out two-dimensional image edge detection on the multi-view image to obtain an edge image and calculating to obtain a structural point cloud; 2, initializing a spherical 3D Gaussian primitive based on the structural point cloud; 3, supervising and optimizing the number and attributes of the spherical 3D Gaussian primitives by using the edge graph; step 4, carrying out linear fitting on the optimized spherical 3D Gaussian primitive based on interpolation rough; and step 5, further optimizing a straight line fitting result to a third-order rational Bezier curve to obtain a final multi-view picture three-dimensional edge reconstruction result. According to the method, a spherical 3D Gaussian primitive is introduced as a three-dimensional edge representation medium, and the attribute of the spherical 3D Gaussian primitive is optimized by using edge detection information of a two-dimensional image, so that three-dimensional edge information is extracted from the two-dimensional image; the 3D Gaussian splashing technology is used for monitoring spherical 3D Gaussian attributes, and the consistency of spherical 3D Gaussian primitives in two-dimensional rendering and three-dimensional representation is guaranteed.

The invention provides a plastic greenhouse image extraction method and system based on multi-feature deep fusion, and belongs to the field of machine vision and image extraction. The method comprises the following steps: determining a research area and year, acquiring a remote sensing image and multispectral remote sensing data, and extracting a remote sensing image sample and an index sample with the same resolution; constructing a greenhouse image extraction model and a boundary learning model; performing down-sampling on the index sample, and inputting the index sample into a multispectral branch of a greenhouse image extraction model to obtain multispectral features; performing down-sampling on a remote sensing image sample and then inputting the sample into five convolutional layers of semantic branches; respectively acquiring first and second semantic features based on the second and fifth convolutional layers; inputting the remote sensing image samples into detail branches, and extracting detail features of the plastic greenhouse; and fusing the fused edge image, the multispectral features, the first semantic features, the second semantic features and the detail features, and extracting to obtain a plastic greenhouse image. According to the method, the problems of boundary blur and contour adhesion are solved, and the identification capability is improved.

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 recognition method and system for stone slab crack detection, and the method comprises the steps: carrying out the preprocessing of a color image of a stone slab, and obtaining a gray image; calculating the gradient amplitude of the grayscale image, determining an optimal threshold value, and obtaining a preliminary edge image; calculating the first order gradient and direction of the preliminary edge image, constructing a Hessian matrix and generating a crack response image; performing edge detection on the crack response image to generate an initial edge image; performing connected domain analysis on the initial edge image to generate a final edge image; and extracting coordinates of crack points from the final edge image, mapping the crack points to the stone slab color image, and performing visual marking to generate a marked image. According to the method, high-precision extraction and fine distinguishing of the cracks under the background of complex stone textures are achieved, the limitation that the cracks and the patterns are difficult to separate in a traditional method is effectively overcome, meanwhile, crack marking is directly carried out on the stone slab original drawing, and the accuracy and practicability of stone slab quality detection are remarkably improved.