85 results about "Silhouette edge" patented technology

The invention relates to the technical field of computer vision, in particular to a jujube tree disease and insect pest recognition method and system combined with a vision technology, and the method comprises the steps: extracting closed edge contours of a disease leaf gray image, and obtaining a contour skeleton of each closed edge contour; based on the graphic features of each contour skeleton, obtaining a to-be-recognized region, analyzing the distribution of gradient amplitudes of the skeleton contour edge and inner pixel points and the gradient amplitude distribution of the skeleton contour outer pixel points, and obtaining an edge transition value of each to-be-recognized region; based on the overall distribution characteristics of the gradient amplitudes of all edge pixel points of the skeleton contour of each to-be-identified area, obtaining an edge energy value of each to-be-identified area; and determining an edge distinguishing value of each to-be-identified area, and obtaining a disease identification result of each to-be-identified area. The invention aims to improve the identification capability of the jujube brown spot and the jujube gray leaf spot and improve the identification precision of disease detection.

The invention relates to the technical field of image processing, in particular to a defect detection method, system and equipment for an optical protective film, and the method comprises the steps: obtaining each frame of film grayscale image, and extracting a film region in each frame of film grayscale image and all contour edges of the film region; determining a matched contour edge corresponding to each contour edge in the film area of each frame of film grayscale image; determining the relative displacement difference and the motion anomaly of each contour edge in the film area of each frame of film grayscale image; determining the distortion possibility degree of each contour edge in the film area of each frame of film grayscale image; and determining the film bending degree and the defect judgment coefficient of each contour edge in the film area of each frame of film grayscale image, and performing defect detection on the surface of the optical protective film. According to the invention, defects and light spots on the surface of the optical protective film can be distinguished more accurately, and the detection precision of the defects on the surface of the optical protective film is improved.

The invention discloses a high-definition image optimization method for an intelligent networked automobile, relates to the technical field of image optimization, and solves the problems of numerical deviation and blurring of the contour edge of an object caused by gray value overlapping of corresponding areas of front and rear frames of images due to object movement. According to the method, the pixel value difference between adjacent frames is fully utilized, gradient calculation and contour area judgment are combined, the dynamic object can be accurately positioned, compared with a traditional method, misjudgment and missed judgment in a complex environment can be reduced, a reliable basis is provided for subsequent optimization, and the sensing accuracy of an intelligent networked automobile to the surrounding dynamic environment is improved; abnormal contour lines can be quickly identified by comparing gradient features of contour areas of similar dynamic objects in continuous frames; according to the method based on gradient column difference analysis, gray scale deviation generated by pixel point overlapping can be sensitively captured, contour blurring caused by foldover is avoided, and the definition and integrity of the edge of a dynamic object in the image are remarkably improved.

The invention relates to the field of image processing, in particular to a workpiece feature recognition method for a CNC machining center, and the method comprises the steps: obtaining the LAB value of each pixel point of a workpiece image to be machined and the gradient value of each pixel point, and traversing the pixel point distribution complexity of each neighborhood window of the pixel points, selecting a neighborhood window corresponding to the maximum pixel point distribution complexity as a target window of the pixel points, and taking the product of the calculated color difference and gradient difference between the pixel points and other pixel points in the target window and the window distribution complexity of the target window as the local saliency of the pixel points; screening all edge points based on the local saliency value to obtain a contour edge image; and performing matching in a workpiece database based on the contour image so as to match a corresponding processing procedure. According to the method, the edge points are screened in combination with the local significance of the pixel points and the optimal significance threshold, the contour edge of the workpiece can be extracted more accurately, and then the accuracy of workpiece feature recognition is improved.

The application relates to the technical field of computer vision, in particular to a jujube tree disease and pest identification method and system combined with visual technology, which comprises the following steps: extracting a closed edge contour of a disease leaf gray image, and obtaining a contour skeleton of each closed edge contour; based on the graphic features of each contour skeleton, obtaining a to-be-identified region, analyzing the gradient amplitude distribution of the skeleton contour edge and the inside pixel points, the gradient amplitude distribution of the skeleton contour outside pixel points, and obtaining an edge transition value of each to-be-identified region; based on the overall distribution features of the gradient amplitude of all edge pixel points of the skeleton contour of each to-be-identified region, obtaining an edge energy value of each to-be-identified region; determining an edge distinguishing value of each to-be-identified region, and obtaining a disease identification result of each to-be-identified region. The application aims to improve the identification ability of jujube brown spot disease and jujube gray spot disease and improve the identification precision of disease detection.

The invention provides a multi-module fusion self-supervision denoising method for an industrial CT image, and belongs to the field of digital image processing and industrial nondestructive testing. In order to solve the problems of detail loss, poor denoising effect and the like in an industrial CT image denoising process, the invention provides a self-supervised denoising model which takes U-net as a trunk network and fuses an edge enhancement module, a convolution block attention module and a convolution attention fusion module. Meanwhile, by means of a mixed loss function composed of structural similarity index loss, mean square error loss and ResNet perception loss, the noise level of the industrial CT image can be remarkably reduced, meanwhile, the integrity of the object structure and the contour edge is accurately kept, and the image quality is effectively improved.

The invention relates to a method for determining the layer thickness of an at least partially transparent surface coating on the surface of a substrate, using a measuring device for emitting focused radiation. The invention is characterized in that the distance between the surface of the substrate and the measuring device for emitting focused radiation is changed by a relative movement between the measuring device and the substrate, wherein the image of the radiation is captured in different relative positions, wherein in the case of an image with a clear outline edge, focusing is inferred at the interface between the surface coating and the substrate and the current relative position is stored as the first position, and wherein in the case of an image surrounded by scattering effects, focusing is inferred at the interface between the surface coating and the air and the current relative position is stored as the second position, after which the difference between the first and second positions is used to determine theTaking into account the refractive index of the transparent surface coating, the layer thickness of the at least partially transparent surface coating is determined.

The invention provides a circular curve burr detection method and system based on multi-scale phase consistency and a contour model, and a medium, and the method comprises the steps: constructing a multi-scale Log-Gabor filter group, and carrying out the convolution calculation of an input image, and obtaining a response vector; calculating phase consistency measurement of each pixel point, extracting an initial edge point set, fitting an initial circular contour, performing energy minimization evolution on the initial circular contour based on an active contour model, and fitting a real contour of burrs; constructing a two-dimensional feature vector; carrying out fusion discrimination on the two-dimensional feature vectors based on a linear weighted score function, and outputting a burr detection result; according to the method, phase consistency is used as a core image feature, so that the detection algorithm has natural invariance for uneven illumination, brightness change and contrast difference, and under the scene of failure caused by poor image quality in the prior art, the contour edge can still be stably and completely extracted, and the omission ratio is remarkably reduced.

The invention belongs to the technical field of condition judgment, and particularly relates to an electrode foil corrosion condition judgment method and system, and the method comprises the following steps: S1, obtaining a grayscale image of a to-be-analyzed electrode foil; constructing a structure tensor based on the composite gradient reflecting the local brightness and texture information of the pixel points, determining an anisotropic diffusion coefficient according to the structure tensor, and performing iterative anisotropic diffusion filtering processing on the grayscale image by using the anisotropic diffusion coefficient to obtain a filtered image; and S2, fusing the anisotropic diffusion coefficient determined for each pixel point with the pixel intensity of the filtered image to obtain a high-contrast corrosion significance map. According to the method, the contour edge information of the corrosion area can be kept while the complex texture and noise of the background area of the electrode foil image are smoothed, the contradiction between denoising and edge protection of a traditional filtering method is solved, the result of the corrosion condition is more reliable, and the automation level of electrode foil product quality detection is improved.

The invention relates to the technical field of image recognition, in particular to a wild animal behavior recognition method and system based on visual recognition. The method comprises the following steps: acquiring continuous image frames, analyzing gray direction changes of pixel blocks in adjacent frames, extracting low-photosensitive texture features and forming a texture change track; then fusing the texture trajectory with the geometric offset trend of the animal contour edge points to generate a behavior dynamic fusion feature capable of representing the action direction and intensity; and carrying out continuity calculation on the fusion features, and judging whether the action meets a behavior triggering condition based on a change continuity index, thereby realizing automatic identification and output of the wild animal target behavior. According to the method, the texture change track and the contour geometric deformation direction are fused, stable recognition of continuous actions under the low-light condition is achieved, and the wild animal behavior recognition result is more accurate and reliable and has the quantitative judgment capacity.

The invention provides an image processing method, electronic equipment and a storage medium. The image processing method comprises the steps of performing feature extraction on a standard workpiece image, and constructing a data model based on extracted feature data; matching the data model with a to-be-detected image by adopting an edge feature matching algorithm, and determining positioning information of a workpiece in the to-be-detected image; performing contour extraction on the to-be-detected image according to the positioning information of the workpiece to obtain a mask pattern of the workpiece; and optimizing the mask pattern by using the edge information of the to-be-detected image to obtain a contour edge point set of the workpiece. The method can improve the extraction accuracy of the contour boundary of the workpiece.

The invention relates to a method, a ray tracing system and a storage medium for performing an intersection test. A ray tracing system and a computer-implemented method for performing an intersection test for a ray bundle with respect to a box are described. Contour edges of the box are identified from a perspective of the ray bundle. For each of the identified contour edges, a component of a vector providing a boundary to the ray bundle is obtained and it is determined whether the vector passes inside or outside the contour edge. A result of the intersection test for the ray bundle with respect to the box is determined using the result of the determination whether the vector passes inside or outside the contour edge for each of the identified contour edges.

This invention relates to the field of image data processing technology and discloses a feature fusion method for multi-target detection and classification of text images. The method includes: acquiring a low-level spatial detail feature tensor and a high-level global semantic feature tensor and aligning the spatial axis resolution to form a feature tensor pair; calculating the channel dot product of the feature tensor pair to obtain a spatial feature modulation matrix; using the element values ​​of the spatial feature modulation matrix as feature flow control weights, combining the feature tensor pairs through a nonlinear gated mapping, and outputting the final multi-target detection and classification fusion feature. This invention establishes a gated constraint boundary through the spatial feature modulation matrix, suppresses high-level feature blurring, strengthens pixel contour edges, eliminates text target feature diffusion and crosstalk, reshapes semantic causal correspondence, and enhances detection recall and classification accuracy.

The application discloses a cross-modal pedestrian re-identification method and system based on contour map guidance, and belongs to the technical field of pedestrian re-identification. The application adopts an additive and multiplicative collaborative operation mode to weight image features on corresponding contour features. The additive and multiplicative collaborative operation can combine the respective advantages of image features and contour features and save the original image information. A feature energy function is defined to weight the fused features. The feature energy function calculates the dispersion degree of each neuron of the fused features through a mean square deviation. After being weighted by the energy function, the features at the contour edge position will generate greater weights. The features at the contour edge position obtain more attention in subsequent calculations, and the obtained contour guided features and original image features are used for loss calculation together, so that the recognition accuracy of the cross-modal pedestrian re-identification is improved. The problem that the difference between multi-modal images reduces the pedestrian re-identification accuracy in the prior art is solved.

The application relates to a method, system, device and storage medium for image background light illumination homogenization, which comprises the following steps: step S1: inputting an original image, obtaining a rectangular mask, and performing rank filtering on the original image using the rectangular mask to obtain a first image; step S2: performing mean filtering on the first image to eliminate high-frequency gray values in the first image and obtain a second image; and step S3: subtracting the second image from the original image to obtain a final processing image, and scaling the gray value of the final processing image to a preset value. In the application, the original image is filtered using the rectangular mask, which can reduce the difference of the gray values at different positions of the original image; the second image is subtracted from the original image to obtain a third image, which is used to obtain the gray difference value of each pixel point of the second image and the original image, highlight the contour edges in the image, and greatly reduce the problem of uneven image brightness.

A multi-point milling path design method for the full surface feature structure of thin-walled spherical shell micro-components is disclosed, relating to the field of micro-component processing technology. This invention can meet the requirements of high-precision, stable, and controllable removal of full surface feature structures of thin-walled spherical shell micro-components under micro-scale constraints. Key technical points: The method includes spatially uniform distribution of feature structures, micro-structure processing sequence planning, and multi-point milling path generation. The thin-walled spherical shell micro-component to be processed is observed offline using an optical microscope to obtain the coordinates of feature points on the contour edge of the micro-component, and the diameter of the micro-component is obtained by fitting. Based on the secondary development platform of UG software, a micro-structure processing sequence planning method driven by the shortest processing path is established to complete the above-mentioned full surface micro-structure processing sequence planning of the micro-component. Based on the point set coordinates optimized by the spherical point set uniform distribution iterative algorithm and the processing sequence driven by the "shortest processing path," combined with the configuration characteristics of ultra-precision shape control machining equipment and the micro-structure processing requirements, the coordinates of several uniformly distributed point sets with the center of the micro-component as the origin are transformed into a machining coordinate system with the tool setting point as the origin, obtaining the original coordinates of the micro-structure point sets and the corresponding feature angles. This invention is used for full-surface milling of thin-walled spherical shell micro-components.

The embodiment of the application provides a wire harness cutting path correction method, which comprises the following steps: S1: collecting a wire harness cutting area image; S2: inputting the image into an improved SegFormer-B2 deep learning model, and the improvement measures comprise: extending three independent convolution output heads in parallel at the end of the original decoder, which are used for synchronous inference and output three target feature maps: a mark point heat map, a wire harness contour edge map and a pixel direction field; S3: fusing the mark point heat map and the wire harness contour edge map, and using the geometric prior provided by the pixel direction field to reconstruct the actual position path of the wire harness; S4: comparing the actual position path with a preset cutting path, calculating the path deviation, and generating corresponding motion control instructions to drive the cutting equipment to correct and cut. The method effectively overcomes the recognition instability problem caused by a complex working scene, significantly improves the precision of wire harness cutting, and improves the production quality and efficiency.

The application provides an industrial CT image coupling artifact multi-scale generative adversarial network correction method, and belongs to the field of digital image processing. The method aims at the problem that image degradation caused by the coupling of various artifacts in industrial CT imaging is difficult to correct effectively, and provides a multi-scale generative adversarial network correction method. The feature pyramid structure is used to help the network capture more comprehensive artifact feature information, and a multi-scale discriminator is used to build a generative adversarial network framework, so that the generated artifact-removed image is clearer and more real. The method has strong coupling artifact correction capability, and can better restore the detail information of the image, thereby ensuring the accuracy of the geometric size and contour edge of the part in the CT image.

This invention discloses a two-branch oocyte target detection method and system for motion blur. Addressing the motion blur problem caused by equipment vibration and oocyte micro-movement during oocyte retrieval, this invention constructs an end-to-end detection model comprising a clear branch and a blurred branch. Each branch includes a lightweight feature recovery network, a multi-band feature fusion module, a frequency structure enhancement module, and an oocyte target detection network. During the training phase, feature domain deblurring loss, feature consistency self-supervised loss, and oocyte detection loss are used for joint training of the two branches, with the clear branch's features supervising the learning of robust feature representations in the blurred branch. During the inference phase, only the blurred branch outputs the detection results. The multi-band feature fusion module repairs degraded features such as contours, edges, and textures through frequency domain decomposition and targeted enhancement, while the frequency structure enhancement module compensates for missing structural information through a cross-attention mechanism. This invention effectively solves the problem of decreased detection accuracy caused by motion blur.

The invention relates to the technical field of robots, in particular to an intelligent rescue robot which comprises a data acquisition module used for acquiring a field image of a task environment to be executed; the data analysis module is used for determining the qualification of the contour edge of the field image based on the edge contour continuity evaluation value of the field image, and determining whether the feature is a human body feature according to the human body contour evaluation value of the feature of the field image; the data adjustment module is used for determining an adjustment mode according to the ratio of the edge contour continuity evaluation value to a preset edge contour continuity evaluation value on the basis of the condition that the contour edge of the field image is unqualified; the data processing module is used for determining whether a fuzzy region exists in the field image or not based on the color characterization parameter of the field image and determining whether the fuzzy region is a shielding region or not according to the ambiguity evaluation value; and the data optimization module determines an optimization mode according to a difference value between the ambiguity evaluation value and a preset ambiguity evaluation value. According to the invention, the identification efficiency of the intelligent rescue robot is improved.

The invention discloses a transparent element assembling method and system, and the method comprises the steps: firstly building an ideal three-dimensional model of a transparent element and a to-be-assembled part, then measuring the posture of the transparent element (the posture comprises a pitch angle, a roll angle and a depth distance), and adjusting the posture of the transparent element to a to-be-assembled posture based on the ideal three-dimensional model, it is ensured that the initial posture of the element in the three-dimensional space meets the assembly requirement; secondly, the two-dimensional relative poses (including the rotation angle and the translation amount) of the transparent element and the contour edge of the part to be assembled are detected, and the position deviation of the transparent element and the contour edge of the part to be assembled in the plane is accurately obtained; finally, according to the detected two-dimensional relative poses, the relative positions of the transparent element and the to-be-assembled part are adjusted so that the transparent element and the to-be-assembled part can reach the proper to-be-assembled position, according to the method, through staged posture adjustment and posture detection, the positioning problem in the transparent element assembling process is effectively solved, and the method can be widely applied to automatic assembling scenes of various transparent elements.

The present application relates to the technical field of mask lithography, and discloses a hot spot layout correction method, device and equipment in a mask layout and a storage medium, the hot spot layout correction method comprises determining a hot spot layout in an initial mask layout; determining the environmental graphic parameters of each contour edge segment in the hot spot layout; constructing a basic optimization objective function according to the edge placement error varying with the displacement amount of the contour edge segment; superimposing an optimization compensation term on the basic optimization objective function according to each environmental graphic parameter to obtain an optimization objective function; optimizing the displacement amount of each contour edge segment according to the optimization objective function to obtain the corrected displacement amount corresponding to each contour edge segment, so as to determine the corrected layout of the hot spot layout. The present application realizes the automatic correction of the hot spot layout without manual operation, improves the correction efficiency of the hot spot layout, and ensures the accuracy and reliability of the correction result.

This invention relates to the field of image processing technology and provides a motion control method and apparatus. The motion control method includes: acquiring a real-time image of an object to be clamped through an industrial vision module; extracting the initial object contour edge poles corresponding to the object to be clamped from the real-time image; identifying feature points located around the object to be clamped; determining the actual object contour edge poles based on the spatial distribution relationship between the feature points and the initial object contour edge poles; calculating the clamping position and motion path based on the position information of the actual object contour edge poles; and controlling the servo motion module to perform object clamping motion based on the clamping position and the motion path. The motion control method provided by this invention effectively improves the accuracy, flexibility, and efficiency of the clamping process through precise object contour recognition, path planning, and servo control.

The application discloses a kind of digital processing method of space special-shaped curtain wall decorative panel, panel and construction method, including based on modeling graphics, along the contour edge of decorative panel, draw sorting guide line;Determine the geometric center of each panel, and make relative position mark;The position of the geometric center of each described panel is unchanged relative to the sorting guide line, the sorting guide line is converted into straight line, while several described geometric center is flattened into dot matrix graphics;After flattening dot matrix graphics and straight line state sorting guide line are projected into the same plane in world coordinate system, and according to actual size, etc. Enlargement, get flattened plane graphics;Each described panel is placed in flattened plane graphics according to original position distribution one-to-one correspondence, and get processing graphics. Numerous panel individuals can be block linked, the relative position of each panel and arrangement mode are determined, to be more beneficial to processing and arranging, so as to improve panel utilization and reduce material cost.

The present application belongs to the technical field of situation judgment, and particularly relates to a kind of electrode foil corrosion situation judgment method and system, comprising the following steps: S1, the gray scale image of electrode foil to be analyzed is obtained;Based on the composite gradient reflecting the local brightness and texture information of pixel point, the structure tensor is constructed, and the anisotropic diffusion coefficient is determined according to the structure tensor, the gray scale image is processed by iterative anisotropic diffusion filtering using the anisotropic diffusion coefficient, and the filtered image is obtained;S2, the anisotropic diffusion coefficient determined for each pixel point is fused with the pixel intensity of the filtered image to obtain a high-contrast corrosion significance map.The present application can smooth the complex texture and noise in the background area of electrode foil image while maintaining the outline edge information of the corrosion area, solve the contradiction between denoising and edge preservation in traditional filtering method, make the corrosion situation result more reliable, and improve the automation level of electrode foil product quality detection.

The application provides a power line identification method, system, device and storage medium, and belongs to the technical field of power system detection. The method comprises the following steps: acquiring a to-be-detected image containing a power line; constructing a phase filter stretching kernel function based on a phase shift kernel function and a convolution theorem; inputting the to-be-detected image into the phase filter stretching kernel function, using the phase filter stretching kernel function to perform stretching transformation on the to-be-detected image, converting the to-be-detected image in the frequency domain into an angle image in the spatial domain, and extracting a contour edge in the angle image; superimposing the to-be-detected image and the angle image containing the contour edge to obtain a superimposed image, using relative total variation (RTV) to perform sharpening enhancement processing on the superimposed image, and identifying the power line. The application can improve the identification accuracy of the power line.

This invention discloses an online measurement method for thread parameters of precision parts based on machine vision. The method is executed by a detection module and includes: acquiring multiple images of the same precision part; dividing each image into several local regions; constructing multiple candidate local region sets for local regions at the same spatial location; performing multiple verifications on multiple local regions in each candidate local region set to select a target local region; stitching together the target local regions determined at each spatial location to generate a stitched thread image; and determining the thread parameters of the precision part based on the stitched thread image. This invention fundamentally avoids edge grayscale gradient distortion caused by cross-image pixel mixing, effectively ensuring sub-pixel accuracy of thread contour edge positioning, thereby improving the measurement accuracy of thread parameters such as thread angle and pitch.

The invention relates to the technical field of image processing, in particular to an intelligent grading method for procambarus clarkii based on an image processing technology. The method comprises the following steps: acquiring a back trunk area and a side trunk area in a back image and a side image of procambarus clarkii; obtaining the contour edge of the back torso area and edge segments in the back torso area, and screening the edge segments to obtain suspected abdominal segment edge segments; the suspected abdominal segment edge section closest to the tail direction and the contour edge of the back trunk area form a suspected tail segment area, then whether the shrimp tail is curled or not is judged, and if yes, a first key point and a second key point are obtained; thirdly, acquiring a third key point and a fourth key point on the contour edge of the side trunk area; and analyzing the edge section between the third key point and the fourth key point to obtain the curling degree of the shrimp tail, further correcting the area of the back trunk area, and judging the grade of the procambarus clarkii according to the corrected area. According to the method, the grading accuracy of the procambarus clarkii can be improved.

The invention discloses a belt tearing lightweight detection method and device based on super-resolution reconstruction and an ME-DETR model. The method comprises the steps that an original low-resolution image set is expanded through a MultiEM graph enhancement algorithm, and a belt operation environment is simulated; inputting a multi-scale effective information enhancement network, extracting multi-level features of the feature map through multi-scale residual distillation, capturing multi-scale local and non-local self-similarity information by using an efficient and high-similarity transmission attention mechanism, and performing enhancement processing on the contour edge of the belt image; inputting an ME-DETR target detection model, and outputting an enhanced multi-scale feature map by using a multi-element backbone network through multi-scale feature extraction, depth separable convolution optimization, layered grid block enhancement and a multi-scale gating attention mechanism; and performing feature coding through an efficient hybrid encoder fused with an efficient additive attention mechanism, and outputting multi-scale features fused with local details and global information to a decoder.