47 results about "Morphological processing" patented technology

The application discloses a kind of semi-structured orchard field ridge area detection method, this method is by picking robot along field ridge with stereo vision camera Real-time acquisition field ridge area image, utilize field ridge shadow area and the different of non-shadow area color and gradient field feature, combined with Gaussian mixture model can realize the detection of shadow area, again through the shadow color weighting compensation algorithm proposed in this paper to remove shadow area, finally through to image denoising filtering, by to image secondary segmentation, again after morphological processing, finally get complete field ridge area;Finally, through edge detection operator to the image after segmentation Edge detection, again to edge point optimization fitting, based on least squares method is carried out edge point fitting, obtains the edge line of field ridge area.This method can be under any illumination conditions 100% detection field ridge area.

The present application relates to the technical field of intelligent detection and automation, and discloses a kind of surface cleaning device's jet flow nozzle fault on-line detection method.Just determine the pixel cleaning area of each nozzle in image;Collect historical images before and after strip cleaning and construct difference map based on color difference, establish surface cleaning quality detection model in combination with the highest and lowest original score, and set qualified standard;Real-time acquisition of images before and after cleaning input model score, identify unqualified images according to qualified standard and mark unqualified area;Semantic segmentation and morphological processing are carried out on the marked area, the contour boundary is extracted, and the faulty nozzle is located in combination with the pixel cleaning area.The method solves the problem of difficult positioning of nozzle fault in existing high-pressure abrasive water jet surface cleaning device.

The application belongs to the technical field of industrial system information, and provides an industrial target recognition and accurate positioning method based on an improved Hough algorithm, which comprises the following steps: adjusting parameters of an industrial camera to obtain clear image data of a workpiece, and pre-processing the image data; performing edge detection on the pre-processed image based on a morphological processing algorithm and a Canny edge detection algorithm, obtaining edge information, and judging whether there is edge loss; for the edge loss condition, performing target recognition and positioning based on an improved Hough algorithm or a template matching algorithm, and obtaining the center point coordinates of the target object; the application can more accurately and effectively detect the features of the target boundary, accurately deduce the boundary loss condition, and then realize accurate positioning of the detection target.

The application discloses a drug diffusion degree determination method and device, electronic equipment and storage medium. The drug diffusion degree determination method of the embodiment of the application comprises the following steps: performing image morphology processing on a drug diffusion target region image, converting the drug diffusion target region image and the image after morphology processing into a gray scale image form, obtaining a target region gray scale image and an image gray scale image after morphology processing, subtracting the image gray scale image after morphology processing from the target region gray scale image to obtain an edge contour of drug diffusion, and determining the drug diffusion degree according to the edge contour of drug diffusion and the target region gray scale image. The drug diffusion degree determination method of the embodiment of the application can accurately determine the diffusion degree of a drug in a target region in a human body, and provides an accurate reference for doctors, so that the doctors can quickly and accurately master the drug diffusion degree.

The application discloses a wafer defect detection method and system based on edge features and gradient boosting trees, and belongs to the technical field of semiconductor manufacturing. The method first carries out denoising and gray scale normalization preprocessing on a wafer image, then extracts gradient, direction consistency, texture and edge density features through multi-scale edge detection to represent the difference characteristics between low-contrast linear defects and point defects and construct a pixel-level feature vector; gradient boosting tree model is used to realize pixel-level defect classification, and the final result is output in combination with morphological processing and connected domain geometry checking. The application can represent the difference characteristics between low-contrast linear defects and point defects, and is suitable for on-line defect detection of semiconductor wafers.

This invention discloses a real-time monitoring method for vehicle lane-crossing behavior based on image semantic segmentation, comprising the following steps: acquiring traffic video data to obtain a road image sequence and performing preprocessing; inputting the image semantic segmentation model for semantic segmentation processing to obtain a semantic segmentation result image; performing morphological processing and connected component analysis to obtain the set of pixel boundaries of the vehicle region and the set of pixel boundaries of the lane lines; calculating the spatial position data of the lane lines and performing perspective correction processing to obtain perspective-corrected lane line spatial position data; generating the position region of the vehicle region in the image coordinate system; performing target association matching to generate vehicle motion trajectory data; establishing spatial relationship data and forming relative position changes; performing temporal consistency analysis and real-time determination, and outputting the vehicle lane-crossing behavior detection result, thereby achieving continuous, accurate, and real-time monitoring of vehicle lane-crossing behavior.

The present application belongs to the technical field of image data processing, and particularly relates to a brake pad surface damage detection method and system based on machine vision, which comprises the following steps: acquiring an image of the brake pad surface and calculating the gradient vector of each pixel point; constructing a gradient interaction field based on the strength of the gradient amplitude and the gradient direction angle to obtain the structural saliency of each pixel point; performing path search along the local principal direction, accumulating the structural saliency and direction consistency weight of the pixel points on the path to obtain the linear propagation potential of each pixel point; and performing adaptive threshold segmentation and morphological processing based on the linear propagation potential map to realize accurate detection of the damage area on the brake pad surface. The present application solves the technical problems of strong noise interference on the brake pad surface and difficulty in detecting the intermittent characteristics of small cracks, and improves the robustness and accuracy of damage detection.

This invention discloses a method, system, medium, and product for detecting terminal crimping imaging quality, relating to the field of imaging quality detection. In this method, deep learning feature extraction is combined with image processing technology. Through the synergistic effect of a dynamic threshold adjustment strategy library and an edge detection morphological processing rule library, a complete adaptive detection process is formed. This invention can automatically adjust detection parameters based on real-time image brightness conditions and terminal specification information, ensuring stable detection accuracy under different lighting environments and diverse terminal types. This reduces the problems of unstable detection accuracy and poor versatility caused by fixed parameters in existing technologies, improving the reliability and practicality of terminal crimping quality detection.

The embodiment of the application provides a method and device for obtaining opacity map in a portrait matting process, and belongs to the technical field of image processing, wherein the method comprises the following steps: obtaining an original image to be matting; performing portrait and hair segmentation and binarization processing on the original image to obtain a first binary mask and a second binary mask; performing adaptive morphological processing on the binary mask to obtain an initial trimap of a portrait foreground; performing an iteration process, which comprises the following steps: taking the initial trimap or the trimap obtained in the last iteration process as prior information, inputting the original image and the prior information into a trained portrait matting network model to obtain an output opacity map; performing segmentation, binarization processing and adaptive morphological processing on the opacity map to obtain a trimap of the current iteration process; and when the opacity map output by the portrait matting network model meets a benchmark real data standard or reaches a maximum iteration number, outputting the opacity map of the last iteration process.

This invention relates to a method for extracting the center of a circular hole on a calibration plate, comprising: projecting a point cloud after planar fitting processing onto an image plane to generate a point cloud image, wherein the point cloud is the point cloud of the calibration plate acquired by a lidar; and performing morphological processing on the point cloud image to extract the center of the circular hole on the calibration plate. This method is highly versatile, requires no excessive parameter settings, and is not limited by the size of the calibration plate. Compared with previous traditional methods, it yields a more accurate center.

This invention discloses a two-stage hybrid heuristic method for crack path detection in solar cells. The method includes: acquiring an electroluminescence image; generating a crack potential map (CPM) that suppresses background noise and enhances crack features using cascaded morphological processing; dividing the CPM into a coarse-grained region of interest (ROI) grid; searching for a globally optimized ROI path that satisfies spatial continuity constraints in the grid space using a first heuristic algorithm; generating a constrained pixel search space based on the regions contained in this path and after region expansion processing; and finally, searching for the optimal crack path within the constrained space using a second heuristic algorithm based on a movement cost that is non-linearly negatively correlated with the pixel potential value. This invention effectively reduces the search space and, combined with a cost function modified for crack features, achieves high-precision, topologically complete crack path extraction without the need for a high-performance GPU.

The present application provides landfill methane source level emission automatic identification and adjacent source correction quantification method and system, concentration and wind field data are collected through underway observation, emission sources are automatically identified through gridding, morphological processing and connected domain clustering, baseline emission rate is obtained by integration with multiple cross sections, and adjacent source interference contribution is deducted based on inverse distance weighting, combined with Monte Carlo uncertainty propagation of five parameters of wind speed, wind direction, concentration, background and cross section distance and four internal diagnostic self-checking, automatic identification of source level emission, adjacent source correction, uncertainty quantification and confidence discrimination in multi-source scene of landfill are realized, subjective bias of manual source identification is eliminated, systematic overestimation caused by plume overlap is inhibited, reliable confidence interval is provided, and implicit quantitative failure results are automatically filtered.

The application discloses a landslide identification method and system and a medium, and belongs to the technical field of geological disaster monitoring and remote sensing image processing; the method comprises the following steps: acquiring a phase gradient graph and a deformation rate graph of a target region; calculating a local adaptive threshold based on the phase gradient graph, and classifying pixels into landslide pixels and background pixels by comparison; generating a geometric boundary box covering each landslide region based on the landslide pixels; extracting a corresponding deformation rate distribution graph by using each boundary box; determining a segmentation threshold based on the gray histogram features of each distribution graph, and extracting a significant deformation region according to the segmentation threshold; performing morphological processing and merging on each deformation region to obtain a result graph indicating the spatial distribution of the landslide region; and the application realizes automatic, high-precision identification and boundary positioning of large-range landslide hazards.

The application discloses a kind of desulfurization absorption tower measuring instrument intelligent calibration method and device, it is related to industrial measuring instrument calibration technical field, wherein method includes: using industrial camera to collect slurry surface image, after lens distortion correction, gray scale and denoising processing, generate clear and distortionless image to be analyzed;Based on preprocessed image, through edge detection, morphological processing extraction and completion foam boundary profile, calculate foam coverage rate index;According to foam coverage rate grading determination slurry operating state and output grade early warning, monitor its change trend, detect when continuous rise trend early warning;Based on foam coverage rate, build correction model, calibrate the density meter, liquid level gauge measurement value disturbed by foam, output real parameter and prompt data reliability;Calibration data and early warning signal are uploaded to power plant distributed control system, complete whole-process application.The application realizes closed-loop intelligent control from slurry state monitoring to instrument calibration.

This invention provides a structure-aware adaptive Gaussian splashing method, belonging to the field of 3D scene reconstruction and rendering technology. First, multi-view images of the scene and camera parameters are acquired, and 3D Gaussian primitives are initialized. Then, a structure-aware mask is constructed using morphological processing and surface normal consistency constraints to enhance the geometric representation of thin-structure regions and occlusion boundary regions. Next, a cascaded multi-resolution optimization strategy is employed to progressively update the Gaussian primitive parameters. Then, the orientation and anisotropic shape of the Gaussian primitives are adaptively adjusted based on rendering feedback. Finally, a multi-index evaluation strategy is constructed, combining uncertainty, color entropy, gradient intensity, and view coverage to complete the importance assessment and pruning of the Gaussian primitives, obtaining an optimized set of 3D Gaussian primitives for target image rendering. This invention can effectively improve the structural representation and detail restoration effects in complex scenes, rendering high-quality scene images.

This invention relates to the field of computer vision technology, and discloses a method and system for detecting moldy sunflower seeds based on computer vision. The method acquires color and near-infrared images of a single-layer sunflower seed sample spread on a sunflower seed production line, performs registration, and extracts the grayscale and texture features of the seed region. Adaptive local threshold segmentation technology, combined with morphological processing, is used to achieve high-precision segmentation of the mold defect region. Furthermore, by calculating the mold texture energy and morphological confidence, and comprehensively considering the texture characteristics and spatial position relationship of the defect region, multi-dimensional features are finally fused to calculate the mold confidence, automatically identifying and judging moldy sunflower seeds, thus improving detection efficiency and accuracy.

The application discloses a bipolar residual candidate area generation and statistical screening method for chip surface defects, and belongs to the field of automatic optical detection of chip and semiconductor device surfaces. The method first acquires a template image and a to-be-detected image of the same type of chip or the same packaged semiconductor device under consistent imaging conditions, and performs gray scale normalization and geometric registration on the to-be-detected image. Secondly, a template structure diagram and a to-be-detected structure diagram are constructed, a positive residual diagram and a negative residual diagram are respectively generated, threshold segmentation, morphological processing and connected domain marking are performed to obtain a candidate defect area. Then, geometric features, differential features, polarity features and context features of the candidate area are extracted, and secondary discrimination is completed through a screening model or a rule base, and a defect position, polarity and a determination result are output. The method is suitable for high-recall preliminary screening of micro-abnormalities such as open circuit, short circuit, material shortage, adhesion and edge collapse.

The application discloses a kind of based on computer vision OpenCV extraction building contour and planar functional feature method, including original image is preprocessed and edge detection, obtain initial edge image, carry out binary and contour search, multiple candidate contours are geometrically approximated and screening processing, according to screening result determine the final building outer contour;Input functional image containing building internal functional partition, and carry out color space conversion and threshold segmentation, binary mask is morphologically processed and contour search, determine the contour of each functional area, according to preset rule, functional area is labeled;The extracted functional area is clustered and analyzed, and the analysis result containing functional contour, area statistics and appearance probability is output.The application can shorten the time of single building contour extraction and single household planar function analysis from day level, hour level to second level to minute level, improve the efficiency of building design, effectively overcome the defects of low efficiency and long cycle of manual operation.

The present disclosure provides a method, device, equipment and medium for optimizing a two-dimensional code image in a reflective scene, comprising: performing gray value weighting calculation on each pixel point in the two-dimensional code initial image to generate a gray processing image; traversing all pixel points in the gray processing image through a preset sliding window, and taking the mean value of the gray values of all pixel points in the sliding window as the gray value of the center pixel point in the sliding window to generate a filter processing image; performing convolution operation on the filter processing image through a preset filter to generate a blur smoothing image; traversing all pixel points in the blur smoothing image through a preset size of an erosion kernel, taking the minimum gray value in the covered area of the erosion kernel as the gray value of the current pixel point to generate a morphological processing image; using an image inpainting algorithm to repair the content of the highlight area; and performing adaptive local mean binarization on the morphological processing image to obtain a two-dimensional code optimization image. Thus, the two-dimensional code recognition efficiency is effectively improved.

The application provides a mold monitoring method and system based on deep learning and multi-scale template matching, and a medium, the method comprising the following steps: constructing a multi-angle multi-size template, acquiring a real-time collected template image or mold image, and pre-processing and enhancing the collected image; performing image matching on the pre-processed and enhanced image through a pyramid algorithm; judging whether the template or mold has defects according to the matching result; if so, performing morphological processing and Canny edge extraction on the defect area, and performing false detection filtering based on geometric features to remove abnormal areas; and positioning the defect area of the template or mold through connected component analysis. Through the fusion of multi-template dynamic management, multi-scale pyramid matching, image enhancement preprocessing, refined post-processing and semi-automatic linkage mechanism, the application realizes millisecond-level response and stable detection in a high-speed and complex environment.

This invention discloses a welding method and apparatus for welding the outlet pipe of a stainless steel centrifugal pump, comprising the following steps: acquiring weld images between the centrifugal pump and the outlet pipe using a binocular camera; preprocessing the weld images to obtain enhanced images; segmenting the enhanced images to separate the weld from the weld images, obtaining an initial weld image; performing morphological processing on the initial weld image to obtain a laser stripe image, and then extracting the weld centerline from the laser stripe image; controlling the trajectory of the welding torch according to the weld centerline to achieve welding of the weld between the centrifugal pump and the outlet pipe. This invention enables automated machine welding of the pump casing and the inlet pipe, reducing labor costs and improving welding efficiency, welding quality, and weld smoothness.

The invention discloses a false target identification method and device based on low-rank sparse decomposition shadow extraction, and belongs to the field of synthetic aperture radar false target identification. Aiming at pain points with inaccurate shadow extraction and poor recognition robustness caused by speckle noise, the method comprises the following steps of: firstly, dividing regions of interest for a deception jamming SAR (Synthetic Aperture Radar) image; then, the CA-CFAR is used for eliminating strong scattering points; converting the image into a low-rank-sparse matrix through translation reconstruction, so that the shadow is in a low-rank state and the noise is in a sparse state; a residual threshold is adopted to drive low-rank sparse decomposition adaptive separation, and sparseness priori is not needed; oTSU binaryzation and morphological processing are carried out to obtain a preliminary shadow; according to a target-shadow fixed geometric angle, clustering correction is carried out, false detection is eliminated, and a final shadow is obtained; and finally, completing identification according to the condition that no shadow is a false target. According to the method, shadow and noise decoupling is realized under the single-channel condition, and the extraction precision and the recognition robustness are remarkably improved.

The present application relates to a kind of electronic cigarette pressure relief direction detection method based on machine vision, belong to machine vision and electronic cigarette safety detection technical field.The method includes: the pre-processing of pressure relief test video;With the relative position of its two-dimensional coordinate system of establishment equipment itself, using deep learning model to locate tobacco stem and mouthpiece;Through threshold segmentation and morphological processing by conversion to HSV color space, robustly extract flame profile and calculate its centroid coordinates;Finally, by calculating the angle between the vector from the origin of coordinate system to the centroid of flame and the reference direction vector, realize the quantitative judgment of pressure relief direction and risk level assessment.The method of the present application shows excellent accuracy and stability in electronic cigarette explosion test, high detection efficiency, simple deployment, result is intuitive and reliable, can provide key technical support for the safety structure design, quality detection and risk assessment of electronic cigarette product, has important application value and popularization prospect in the field of electronic cigarette safety test.

The application provides a remote sensing image landslide patch extraction method and device based on boundary perception, and relates to the technical field of data processing. The method comprises the following steps: constructing a sample image set based on obtained first sample images and second sample images, performing morphological processing on a landslide contour label corresponding to the first sample images to generate a boundary label graph, configuring a patch extraction unit based on a boundary deviation metric corresponding to the sample image set and the boundary label graph, performing image division on a to-be-processed remote sensing image based on different image block specifications to obtain sub-image blocks, inputting the sub-image blocks into the configured patch extraction unit to obtain sub-probability graphs corresponding to each image block specification, performing weighted fusion on each sub-probability graph to generate a landslide probability graph, and performing conversion processing on the landslide probability graph to obtain landslide patch data and output the landslide patch data. The method can ensure the accuracy of landslide area recognition and effectively improve the boundary extraction precision of the landslide patch.

This invention belongs to the field of computer vision technology and discloses a method and system for adaptive segmentation, counting, and centroid localization of blocky materials. The method involves adaptive color perception processing of an original image containing multiple blocky materials, combining pixel color similarity and local and global statistical characteristics of the image to obtain an initial foreground region; multi-scale morphological processing is implemented to remove noise and repair regions; edge information is introduced and combined with a region growing strategy to fill internal holes in the foreground while maintaining the outer boundaries of the actual materials, resulting in a precisely segmented foreground region; contour and concavity feature analysis distinguishes between material-specific concavities and material adhesion concavities, segmenting only in cases of adhesion; adaptive posture confirmation is performed on the effective materials, and the centroid position of each blocky material is calculated, outputting the number of materials and the corresponding centroid localization results. This invention can achieve stable segmentation, accurate counting, and reliable centroid localization of blocky materials under complex backgrounds and multi-material contact conditions.

The application discloses a kind of astronomical imaging stripe noise correction method and system based on low-frequency directionality feature, first, realize column level brightness equalization through robust statistics, preliminarily eliminate global vertical stripe;Again, through the frequency domain analysis of sliding window, extract low-frequency directionality feature and generate heat map, obtain fixed trapezoidal mask after threshold segmentation and morphological processing;Finally, under the constraint of mask, through star point suppression, frequency domain filtering generates stripe template, and the image is compensated and corrected.This method combines spatial equalization and frequency domain template estimation, and accurately constrains the effective correction area through trapezoidal mask, which not only retains the true star point signal, but also efficiently suppresses residual stripe noise, significantly improves the signal-to-noise ratio of astronomical image and the reliability of subsequent celestial body detection.

This invention relates to the field of rock mass structure surface recognition technology, and in particular to a method and device for recognizing rock mass structure surfaces in limestone tunnels. The method includes the following steps: S1, acquiring an image of the limestone tunnel face; S2, inputting the limestone tunnel face image into a pre-trained U-Net-based limestone structure surface recognition model to obtain an image of the limestone tunnel rock mass structure surface; S3, performing trace morphological processing on the limestone tunnel rock mass structure surface image to obtain the skeleton lines of the limestone tunnel rock mass structure surface; S4, straightening the skeleton lines of the limestone tunnel rock mass structure surface to obtain a skeleton line parameter model of the limestone tunnel rock mass structure surface; S5, performing pixel-level statistics on the skeleton line parameter model to calculate the length and apparent dip angle of each skeleton line. Using the method of this invention, a set of efficient and accurate structural surface parameter recognition statistical techniques for limestone areas is ultimately formed.

This invention relates to a method and system for predicting the quality of cold-pressed diamond grinding wheels, and pertains to the field of data processing technology for the quality of cold-pressed diamond grinding wheels. The method includes generating a multilayer powder initial spatial distribution feature matrix containing layer category labels, the final corrected local packing density, and initial contact state identifiers between particles; calculating the principal directions and intensities of interparticle contact forces within each voxel using a force chain network tracing algorithm to generate a time-series sequence of the tensor field of the diamond grinding wheel cold-pressed densification force chain network evolving along the pressing time axis; extracting the pore network by combining the surface temperature field cloud map sequence of the compact with morphological processing to generate a density-pore coupling field of the diamond grinding wheel; and coupling the cohesive zone model with a porous plastic constitutive model to obtain a comprehensive molding quality index, quality grade determination, and optimal process parameter combination. This invention effectively solves the problems of lagging quality prediction, missed detection of interface defects, and reliance on experience for process parameter optimization in existing technologies.

This invention belongs to the field of defect detection technology, and particularly relates to a machine vision-based method for detecting surface defects in common rail pipes. The method first acquires grayscale images of the common rail pipe surface, and then performs illumination uniformity correction using a cylindrical curved surface illumination basis function and a two-color reflection model. A multi-scale sliding window is used to extract the multi-dimensional grayscale feature vector of each pixel. A Gaussian mixture model is used to fit the grayscale histogram to determine the number of clusters, initial centers, and search range constraints. A three-item metric function is constructed, including data fitting, spatial neighborhood consistency constraints, and cluster center anchoring regularization, to iteratively solve for pixel membership and cluster centers. Finally, the defect probability is calculated, and defect classification and information output are achieved through adaptive segmentation, morphological processing, and feature matching. This invention can effectively eliminate illumination interference, improve defect segmentation accuracy and noise resistance, and meet the requirements for high-precision, automated batch inspection of common rail pipes.