42 results about "Active contour model" patented technology

The invention relates to the field of image processing, in particular to a pin shaft forging forming quality detection method and system, and the method comprises the steps: carrying out the image collection of a to-be-detected pin shaft forge piece; then determining a total energy function of the active contour model and determining a neighborhood window, obtaining a structure tensor based on a gradient magnitude in the neighborhood window, and calculating to obtain local gradient direction dispersion; then, a defect edge structure enhancement index is calculated; then calculating a self-adaptive external energy scaling adjustment factor, and fusing the self-adaptive external energy scaling adjustment factor into an energy function of the active contour model to form an improved active contour model; and finally, accurately segmenting and extracting the surface defects of the pin shaft, and carrying out quality detection by combining the extracted defect characteristics. According to the method, by constructing local gradient direction dispersion and defect edge structure enhancement, a self-adaptive external energy scaling adjustment factor is calculated, and an active contour model is improved so as to accurately detect the surface defects of the pin shaft forge piece.

The invention discloses a defect edge extraction method and device based on a terahertz image, terminal equipment and a storage medium, and belongs to the technical field of defect detection, and the method comprises the steps: obtaining an original terahertz image; performing enhancement processing on the original terahertz image to obtain an enhanced terahertz image; segmenting the enhanced terahertz image into a plurality of target areas through a mean shift algorithm; for each target area, through a maximum between-class variance method, calculating segmentation threshold values of a foreground and a background in the target area; according to the segmentation threshold, segmenting a foreground region from the target region as a defect region; and inputting the defect area into an active contour model, so that the active contour model generates a defect edge curve according to the defect area. The problems that in the prior art, a defect area needs to be specified manually in advance, and the defect edge extraction error is large can be solved.

The invention relates to the technical field of image processing, in particular to a non-contact measurement method and system for the thickness of a cable insulation layer, and the method comprises the steps: constructing an energy field according to a gradient amplitude in a cable cross section grayscale image; initializing inner and outer contour lines of the edge of the insulating layer in the grayscale image; performing short-term iterative evolution on the inner and outer contour lines by using an active contour model to quantify the motion uncertainty of each contour point; adaptive adjustment is carried out on the inner contour line and the outer contour line according to motion uncertainty; on the basis of the adaptive adjustment, continuing iterative evolution until the inner contour line and the outer contour line converge; and extracting geometric parameters based on the converged inner and outer contour lines to determine the thickness of the cable insulation layer. According to the technical scheme, accurate segmentation of the inner edge and the outer edge of the cable insulation layer can be achieved, and therefore the thickness of the cable insulation layer can be accurately measured.

The invention relates to the technical field of temperature monitoring, in particular to a severe pneumonia patient body surface temperature distribution monitoring method based on thermal imaging. The method comprises the following steps: solving a dynamic deformation field of the body surface of a patient relative to a standard three-dimensional body surface model through a minimized deformation energy function, and generating an initial monitoring area by using the dynamic deformation field and an internal parameter matrix of a thermal imager; an active contour model is adopted to optimize the boundary of the initial monitoring area, and a self-adaptive optimization monitoring area matched with the current temperature distribution characteristics is generated; if the credibility of the temperature feature vector is low, the temperature feature vector is compensated in combination with vascular active drug infusion information, and a pathophysiological residual feature sequence is obtained. According to the method, an initial monitoring area capable of tracking a target anatomical structure in real time is generated, the spatial consistency and anatomical accuracy of data acquisition are ensured, and a temperature feature sequence for precise time sequence analysis can be generated.

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 present invention relates to the field of image processing, and specifically to a pin forging quality inspection method and system, which collects images of the pin forging to be inspected; then determines the total energy function of the active contour model and determines the neighborhood window, obtains the structure tensor based on the gradient amplitude within the neighborhood window, and calculates the local gradient direction discreteness; then calculates the defect edge structure enhancement index; then calculates the adaptive external energy scaling adjustment factor and integrates it into the energy function of the active contour model to form an improved active contour model; finally, accurately segments and extracts the pin surface defects, and combines the extracted defect features for quality inspection. The present invention improves the active contour model by constructing the local gradient direction discreteness and defect edge structure enhancement, calculating the adaptive external energy scaling adjustment factor, and accurately detecting the pin forging surface defects.

This invention discloses an automated anterior tooth feature parameter extraction method and evaluation system based on computer vision, comprising: acquiring and visualizing a full-mouth scan image of a patient to be processed; cropping the corresponding tooth portion image; using Gaussian blur to eliminate specular reflection; using color segmentation to set thresholds for the H, S, and V channels respectively; separating the teeth and gingiva in each tooth portion image and performing morphological processing on each; extracting the tooth and gingiva portion contours using contour extraction methods; further refining the segmentation accuracy using the Chan-Vese active contour model algorithm and the energy function within the tooth portion contour; extracting corner points using the Shi-Tomasi corner detection method and then clustering them; extracting other important evaluation feature points through curve fitting and extreme value finding methods; and finally calculating relevant objective indicators for tooth evaluation and automatically generating the corresponding tooth score, thus completing the automated anterior tooth evaluation.

The invention discloses a belt slipping fault real-time monitoring and early warning method based on image processing. The method comprises the steps that structured light stripes are projected through a strip-shaped laser, a belt surface stripe image is acquired by combining synchronous acquisition of a camera, and sub-pixel-level center coordinates of the stripes are extracted through graying, threshold segmentation and connected domain filtering processing. And inputting the stripe center coordinate sequence into a lightweight U-shaped convolutional network, reasoning to obtain a belt instantaneous linear velocity vector diagram, and calculating the instantaneous slip degree. And positioning a slip sudden change moment by using a change point detection algorithm, and generating a slip difference chart. And inputting the difference image into a classification network, identifying a fault type, and triggering a narrow-pulse-width LED stroboscopic light source to obtain a high-exposure image. The foreign matter contour is accurately positioned through the active contour model, the risk degree is calculated, a CAN message is generated and uploaded to the cloud, the audible and visual alarm is driven in real time, and timely monitoring and early warning of belt slipping faults are ensured. The safety and the operation stability of the conveying system can be remarkably improved.

The application discloses a LoG operator-based active contour model image segmentation method and system, and belongs to the technical field of computer vision, and the technical points are as follows: firstly, an edge fitting function EFF is used to replace a traditional local data fitting function; secondly, an energy functional model is created; finally, two new functions are introduced to serve as the influence of a distance regularization term and a length term, so as to prevent the level set function from being too flat or too stepped, and to smooth or shorten the curve, thereby realizing image segmentation. The LoG operator-based active contour model image segmentation method and system have the advantages that, compared with a segmentation evaluation database and a model of the prior art, the model has a faster segmentation speed, improved robustness to an initial contour, and strong anti-noise capability.

The present invention discloses a method and device for automatically identifying and segmenting cell somas based on two-photon calcium imaging data. First, a two-photon calcium imaging video of cells is acquired, and image denoising and image enhancement are performed on each frame of the video image. Then, the two-photon calcium imaging video is reduced in dimension to a locally correlated summary image, and a cell recognition algorithm based on multi-scale dot enhancement is applied to the locally correlated summary image to calculate the central coordinates of near-circular structures of different sizes within the imaging region, i.e., the seed points of the cell somas. Finally, for each seed point, the two-photon calcium imaging video is cropped into a block video of a fixed size centered on the seed point, and a cell segmentation algorithm based on an elliptical shape-constrained active contour model is applied to the block video to obtain the contour of the cell soma. The present invention can improve the operation efficiency and reduce the data storage volume.

This paper discloses a signed pressure function segmentation method that combines global and local image information. This method constructs an active contour model that combines global and local image information. First, a hybrid signed pressure function is constructed by linearly combining newly defined global and local grayscale fitting terms. Second, a balloon force function is redefined using global and local image information. Finally, experiments demonstrate that the model constructed by this invention can accurately segment weak boundary and multi-target images. This method not only accurately segments weak boundary and multi-target images, but also performs quickly and exhibits robustness to noise.

The application discloses a monitoring method and system for three-dimensional phenotype parameters of stoma cells of living plants, and comprises the following steps: S1, a plant confocal three-dimensional microscopic imaging and photosynthetic parameter measuring device is used, a self-made photosynthetic leaf chamber is used to realize cooperative monitoring of a down-positioned confocal microscope and a portable photosynthetic instrument, morphological changes of the same stoma under different environments are monitored, and three-dimensional Z-stack images and physiological parameters of a stoma complex of the living plants are synchronously obtained; and S2, a fluorescence image is automatically segmented and three-dimensional surface reconstruction is performed by using a granularity-based active contour model, so that three-dimensional phenotype parameters and pore characteristics of the stoma complex are obtained.

The application belongs to the technical field of image processing, and particularly relates to a cable interlayer structure detection method and system based on image processing, which comprises the following steps: calculating a boundary credibility score of a pixel point based on multi-dimensional features of a cable image to accurately identify a boundary; generating an internal constraint factor in combination with a shape and quality of the boundary; further dynamically adjusting parameters of an active contour model according to the internal constraint factor to accurately extract a closed contour line; and finally evaluating thickness uniformity of an insulating layer based on the contour line. The application solves the problem of limitations of a fixed parameter model in processing a fuzzy or broken boundary by dynamically adjusting model parameters, and improves the accuracy and robustness of cable interlayer structure detection.

The invention relates to the technical field of online detection, in particular to a leather shoe surface quality intelligent online detection method and device based on deep learning. The method comprises the following steps: acquiring a leather shoe rotation multi-frame image, calculating a space-time gradient to generate a comprehensive feature map, and performing initial grid division; extracting grid features, calculating an initial defect probability and a feature entropy, and performing grid splitting, merging or maintaining according to the initial defect probability and the feature entropy; a classification model is selected according to the feature entropy for defect judgment; and carrying out cross-boundary contour search on an adjacent defect grid fusion centroid initialization active contour model, segmenting defects and outputting a vector contour and a classification result. According to the scheme, the three-dimensional defect detection capability can be enhanced, the calculation resource and the detection precision are considered, and the calculation efficiency and the defect judgment accuracy are improved.

This application relates to the field of vision control technology, and in particular to a method and system for compensation and control of CNC machine tools based on industrial vision. The method includes: acquiring real-time images and the position coordinates of the CNC machine tool during the machining process; parsing the current machining instructions to reconstruct the theoretical trajectory, and then constructing an initial contour of an active contour model; performing multiple iterative evolutions on the initial contour using the active contour model until a target contour is obtained; calculating spatial compensation components based on the image deviation of the target contour relative to the initial contour; and adjusting the workpiece zero-point offset of the CNC machine tool using the spatial compensation components. The technical solution of this application can effectively improve the machining accuracy and stability of CNC machine tools in complex machining environments.

The invention relates to the technical field of medical image processing, and discloses an ultrasonic measurement method and device for an inferior vena cava, electronic equipment and a storage medium, and the method comprises the steps: obtaining the ultrasonic image data of the inferior vena cava at the current moment; based on the characteristics of the inferior vena cava in the ultrasonic image data, positioning the area where the inferior vena cava is located by adopting a voting algorithm; on the basis of the area where the inferior vena cava is located, the simplified active contour model is combined, and pipe wall information of the inferior vena cava is extracted; and calculating parameters of the inferior vena cava in a respiratory cycle based on the tube wall information of the inferior vena cava. According to the method, the number of parameters of the active contour model and the calculation complexity are reduced, the extraction speed of the inferior vena cava wall information is increased, and the inferior vena cava wall information can be conveniently obtained in real time. In addition, according to the inferior vena cava wall information obtained in real time, the parameters of the inferior vena cava in one respiratory cycle can be calculated in real time, the accuracy of calculation of the parameters of the inferior vena cava is effectively improved, and the real-time requirement of inferior vena cava measurement can be met.

The present application relates to the technical field of image processing, in particular to a kind of forging heat treatment deformation detection method and system, the method comprises: first, the gray image of forging is obtained, gradient vector, gray saturation and local entropy are calculated, gradient reliability is determined, and local reliable vector is calculated, and guide vector field is generated by tensor diffusion;Then, based on the initialization active contour model, reconstruct its energy function when iteratively updating: external energy term is driven by the guide vector of contour point;Internal energy term is constrained by anisotropic stiffness tensor calculated based on gradient vector and local entropy, and finally the model converges to hot profile and carries out deformation analysis.The method is systematically modified to the internal and external energy terms of traditional model, suppresses halo and haze interference, solves feature confusion problem, and improves the accuracy and robustness of contour detection under high-temperature strong interference environment.

The invention discloses a tumor area automatic detection method and system based on image segmentation. According to the method, continuous two-dimensional slices are extracted and preprocessed by obtaining a three-dimensional pathological tissue slice image sequence; an improved encoder-decoder architecture is adopted, an encoder adopts a three-branch asymmetric structure, a main branch extracts multi-scale features of a current slice, two auxiliary branches extract features of adjacent slices and are embedded into a CBAM attention mechanism, and three-dimensional context perception is enhanced. And the decoder fuses the deep and shallow layer features through up-sampling and jump connection to generate a tumor probability map. Subsequently, a segmentation result is optimized through adaptive threshold segmentation, morphological processing and an active contour model, an accurate tumor segmentation mask is output, and quantitative characteristic parameters are extracted. According to the method, the problems of fuzzy tumor boundary, insufficient feature extraction and difficult diagnosis in a traditional pathological image are effectively solved, and the accuracy and robustness of pathological diagnosis of early infiltration and micro infiltration of the tumor are remarkably improved.

The invention belongs to the technical field of image processing, and particularly relates to an intelligent circuit breaker shell assembly clearance detection method based on image processing, which comprises the following steps of: executing morphological closed operation and connected domain analysis on a circuit breaker shell grayscale image to extract a joint connected domain, and constructing a clearance sequence through a morphological refinement algorithm and topological sorting; acquiring curvature potential energy based on the tangent vector change rate and the linear dispersion of the pixel points, acquiring texture entropy according to the gradient direction and the gradient amplitude, and acquiring a relaxation coefficient according to the curvature potential energy and the texture entropy; respectively translating pixel points on the gap sequence by a preset span value along positive and negative normal directions to obtain offset points, and connecting the offset points to generate a first initial profile curve and a second initial profile curve; improving an energy functional of the active contour model by using a relaxation coefficient and solving a minimum value to obtain a first contour curve and a second contour curve; and determining the gap quality according to the peak value of the maximum distance between the two profile curves. According to the invention, the detection accuracy is improved by improving the energy functional.

The invention discloses an image segmentation method of a parametric type active contour model based on Finsler-Laplacian. According to the method, an anisotropic Finsler norm is introduced under a static external force field, and a new energy minimization problem is provided and comprises a data fidelity term and a regularization term based on the Finsler norm. A corresponding Euler-Lagrange equation is derived through a variational method and is converted into a time-evolved parabolic partial differential equation for numerical solution. A comparison principle is established under the framework of the viscous solution, and the existence of the understanding, the continuity about the space Lipschitz and the continuity about the time consistency are proved. The model has the advantages of being large in capture range, insensitive to initial contours, high in noise immunity, capable of effectively retaining image edge details and the like, and shows excellent performance in medical image and natural image segmentation.

The invention belongs to the technical field of image analysis, and particularly relates to a waste water circulation treatment monitoring method and system for a sand separator, and the method comprises the steps: collecting a surface image of a waste water pool, enhancing the texture difference between foam and waste water through the technologies of local variance calculation, self-adaptive histogram equalization and the like, and calculating the surface image of the waste water pool; accurately segmenting a foam area by using an active contour model based on an enhanced texture gradient map; the foam coverage rate is obtained by calculating the area surrounded by the contours after convergence, the average brightness and the average texture of a foam area are extracted, and a foam thickness index is obtained through weighted fusion; finally, the system executes a hierarchical cooperative control strategy according to the two key indexes of the foam coverage rate and the thickness index, the defoaming device is intelligently started, stopped or adjusted, and automatic, refined and energy-saving management of foam is achieved.

The present invention relates to an active contour model image segmentation method based on K-medoids clustering, comprising: using a K-medoids clustering algorithm to perform binarization processing on an image to be segmented to obtain a fitting result, and acquiring a foreground image and a background image; constructing a pre-segment fitting function based on the binarization fitting result; using a zero level set that meets the Lipschitz condition to replace the contour curve of the pre-segment fitting function to obtain a KM pre-segment fitting function; using a gradient descent method to minimize the energy functional of the KM pre-segment fitting function to obtain a gradient flow equation; using an adaptive regularization function to regularize a data-driven term of the gradient flow equation; using a rulesig function to regularize the zero level set function, using a kernel function to smooth and shorten the curve of the regularized zero level set function, and outputting a level set function as a segmentation result of the image to be segmented.

The application discloses a kind of graphite coiled material morphological anomaly detection methods suitable for winding system, first adjust the position of crossbar connecting block and camera adjustable clamp;When winding system is opened, the intensity image of different polarization angles of coiled material is simultaneously photographed using focal plane polarization camera;The polarization degree difference image is filtered with Gaussian filter, then binarization is carried out, and finally the connected component is extracted;Establish morphological anomaly detection determination threshold, calculate the centroid and boundary box information of connected component;Finally, a circular contour line is defined through the centroid and boundary box information of connected component, and the detection result of the frame morphological anomaly region is output based on the polarization degree image detected by active contour model.The present application can overcome the problem that the existing computer imaging-based detection method is difficult to extract accurate and rich feature points and feature regions when facing graphite coiled material, leading to inaccurate and missed morphological anomaly detection.

The application relates to the field of image detection, in particular to a filter screen quality detection method based on aperture consistency analysis, which comprises the following steps: S1, collecting a filter screen image; S2, optimizing an active contour model based on local feature analysis of the filter screen image; S2.1, obtaining a first optimization factor through gradient space analysis; S2.2, obtaining a second optimization factor through spatial texture information analysis; S2.3, optimizing the active contour model for aperture detection through the first optimization factor and the second optimization factor; and S3, performing filter screen aperture detection and filter screen quality evaluation through the optimized active contour model.

The application provides a high-resolution image segmentation method based on region probability label driven active contour, and solves the problem that an active contour model cannot process high-resolution images.The steps are as follows: firstly, a high-resolution image is down-sampled to obtain images from low to high resolution, and the following processing is performed on the images from low to high resolution: an initial level set function and statistical parameters are obtained, and a region probability label and geometric prior are calculated by using the level set function; secondly, a segmentation contour is updated under the driving of the region probability label, and the corresponding level set function and statistical parameters are updated based on the region probability label update driving; and finally, the level set function and statistical parameters of the current resolution image are transmitted to the next high-resolution image and contour segmentation is performed on the next high-resolution image.The application can greatly reduce the calculation cost and enhance the robustness of the algorithm, so that the high-resolution image can be processed.

The present application relates to the technical field of medical data analysis, and particularly relates to a coronary artery analysis processing method and system based on magnetic resonance CNR value, which comprises the following steps: S1: collecting coronary image data, obtaining pretreatment data after pretreating the collected coronary image data, inputting the pretreatment data into an ROI identification model to obtain a target ROI; S2: using an MRCA method based on the target ROI to obtain an MRCA image of the target ROI, and using a final dynamic adaptability formula to obtain a CNR value of a correlation reference region in the MRCA image. The present application improves the efficiency and accuracy of target region extraction by using the image enhancement and region extraction technology combining the edge preserving normal gradient vector flow with the active contour model, and improves the work efficiency of radiologists by using the method combining the MRCA image and the CNR value to diagnose the coronary artery stenosis degree.

The present application relates to a hip joint classification method, device, and storage medium. The method comprises: obtaining an original ultrasound image of a subject to be tested, extracting an iliac region image corresponding to the hip joint of the subject to be tested from the original ultrasound image, inputting the iliac region image into a preset active contour model for hip joint classification, and obtaining the hip joint type of the subject to be tested; the hip joint classification method provided in the embodiment of the present application is to obtain an iliac region image by segmenting the iliac region from a complete ultrasound image, and then inputting the iliac region image into the active contour model for image analysis, which can greatly improve the processing rate of the active contour model, thereby improving the processing rate of hip joint segmentation, as well as the accuracy of hip joint segmentation, and improving the precision of image processing, thereby comprehensively improving the image processing efficiency of hip joint classification.

The invention belongs to the technical field of image processing, and particularly relates to a cable interlayer structure detection method and system based on image processing, and the method comprises the steps: calculating the boundary credible score of a pixel point based on the multi-dimensional features of a cable image, so as to accurately recognize a boundary; generating an internal constraint factor by combining the form and the quality of the boundary band; parameters of the active contour model are dynamically adjusted according to the internal constraint factors, so that a closed contour line is accurately extracted; and finally, evaluating the thickness uniformity of the insulating layer based on the contour line. By dynamically adjusting the model parameters, the problem of limitation of a fixed parameter model during fuzzy or fracture boundary processing is solved, and the accuracy and robustness of cable interlayer structure detection are improved.

The embodiment of the invention discloses an image segmentation method and electronic equipment. The method comprises the following steps: for each target to be segmented in an image, obtaining an intermediate segmentation result obtained in the process of performing iterative segmentation on each target by using an active contour model, and obtaining parameters according to the image and the intermediate segmentation result, the parameters representing gray information of each target in the image; performing feature extraction on the image by using a first feature extraction network to obtain a first feature map, obtaining a second feature map by using the parameters, the image and a feature function preset for the active contour model, and obtaining a third feature map according to the first feature map and the second feature map; and continuously performing iterative segmentation based on the third feature map to obtain a final segmentation result. According to the technical scheme of the embodiment of the invention, accurate image segmentation can be realized under the condition of a small number of labeled samples.

The present application belongs to the technical field of image analysis, and particularly relates to a wastewater recycling treatment monitoring method and system for a sandstone separator, which comprises the following steps: collecting a surface image of a wastewater pool, using local variance calculation and adaptive histogram equalization and other technologies to enhance the texture difference between foam and wastewater, and then using an active contour model based on an enhanced texture gradient image to accurately segment the foam area; calculating the area surrounded by the converged contour line to obtain the foam coverage, and extracting the average brightness and average texture of the foam area to be weighted and fused into a foam thickness index; finally, the system executes a hierarchical collaborative control strategy according to the two key indicators of the foam coverage and the thickness index, intelligently starts and stops or adjusts the defoaming device, and realizes the automatic, refined and energy-saving management of the foam.