17 results about "Gradient operators" patented technology

The invention relates to a cloth flaw detection method, device and equipment and a storage medium, and the method comprises the following steps: carrying out the multi-angle light source irradiation of the surface of cloth, obtaining a texture image, and carrying out the grid segmentation of the texture image into detection units; and extracting a gray value sequence of each unit, calculating an edge difference by using a gradient operator, generating a texture edge change curve to identify an abnormal wave crest, and determining a defect candidate region. And carrying out local amplification imaging based on the candidate region coordinates, and extracting defect shape feature parameters. The defect types are determined according to the parameters, quality grade division is performed on the cloth according to the parameters, and a quality grade report is finally generated, so that the technical problems of low defect identification precision and easy missing detection of fine defects caused by uneven illumination and texture interference in the existing cloth defect detection method are solved.

The invention discloses an image quality optimization method and system based on multi-scale feature decoupling and dynamic fusion, and the method comprises the steps: carrying out the multi-scale decomposition of an input degraded image, and extracting the feature components of illumination-color, texture-noise and edge-structure; illumination normalization and color fidelity enhancement are realized through illumination estimation and color space transformation; the base layer and the detail layer are separated through edge preserving filtering, and self-adaptive contrast enhancement and noise suppression are carried out on the detail layer; extracting edge information by using a multi-directional gradient operator, and strengthening significant structural features through nonlinear mapping; constructing a lightweight weight learning network, and generating a spatial self-adaptive dynamic fusion weight map according to the multi-scale features; executing progressive three-level fusion according to the dynamic weight, and reconstructing to obtain a high-quality image; according to the method, the image is decomposed into feature components with different physical meanings, targeted optimization and adaptive fusion are carried out, and more accurate and robust image quality improvement is realized.

The application discloses a multi-directional excitation magneto-optical image registration method under hybrid driving, and the method comprises the following steps: collecting the defect magnetic field distribution information of the surface of a ferromagnetic material under multi-directional excitation through a magneto-optical imaging device to obtain a reference image and a floating image; extracting the defect contour of the two images according to a gradient operator; performing spatial transformation on the defect contour of the floating image, and then performing or operation and morphological closing operation on the defect contour of the floating image and the defect contour of the reference image to obtain a defect contour map with closed shape; obtaining the magnetic leakage field distribution map of the defect contour map through defect magnetic leakage field model forward modeling; calculating the similarity measure of the reference image and the magnetic leakage field distribution map generated by the model; and finally continuously updating the spatial transformation parameters through an optimization search algorithm, and the registration is successful when the similarity measure reaches the maximum.

The invention relates to the technical field of image data processing, in particular to a screen support defect positioning method and system based on image processing. The method comprises the following steps of: obtaining local change intensity of each pixel point through gradient amplitude difference of the pixel points in a screen bracket surface image under different scale gradient operators; obtaining suspected pixel points in the screen support surface image through the local change intensity of each pixel point, and calculating the target neighborhood radius of the suspected pixel points and the change rate of the suspected pixel points; calculating the defect degree of the suspected pixel point, wherein the defect degree is positively correlated with the local change intensity and the change rate of the suspected pixel point and the LBP positive value extraction value and the LBP negative value extraction value in the target neighborhood radius of the suspected pixel point; in response to the comparison result of the suspected pixel points in the screen support surface image and the threshold value, the defect area in the screen support surface image is positioned, and the accuracy of screen support defect positioning can be effectively improved.

The application discloses a metal artifact removal method of a CT image, and the method comprises the following steps: constructing an initial adaptive iterative learning model based on wavelet transform, decomposing a CT image by using a target adaptive iterative learning model obtained by optimizing an optimization objective function, calculating the area of a metal artifact, removing the metal artifact in the CT image, and obtaining a CT image with reduced metal artifacts; the initial adaptive iterative learning model based on wavelet transform is used for artifact removal, so that the spatial distribution characteristics of the metal artifact under different domains and resolutions can be fully utilized, the artifact removal is better, and the interpretability is high; in addition, when a first optimization objective function is solved by combining a proximal gradient descent algorithm and a Taylor formula, a proximal gradient operator obtained can be replaced by a simple network module, so that the network can be more easily constructed, and the adaptability of the network is enhanced. The application can be widely applied to the technical field of CT image processing.

The application discloses a laser stripe center line extraction method based on Chebyshev moments, and belongs to the technical field of line structured light vision detection. The Scharr gradient operator is used to detect the laser stripe edge, and then searching is performed along the normal direction of the edge point, so that the laser stripe cross-section gray distribution information is obtained. On the basis of analyzing the laser stripe cross-section gray distribution characteristics, a laser stripe cross-section gray distribution model is constructed, and the laser stripe cross-section center sub-pixel coordinates are solved by using Chebyshev moments, and the laser stripe center line is obtained by connecting the laser stripe cross-section centers. Compared with the Steger method, the gray gravity center method, the spatial moment method and the Legendre moment method, when the gray saturation laser stripe is processed, the center line detection precision can be improved while the algorithm real-time is ensured, the balance between the algorithm precision and speed is realized, and the measurement requirements of the line structured light measurement system can be met in the actual use.

This invention discloses a solution to the joint path and destination planning problem based on a distributed generalized Nash equilibrium algorithm. First, the joint path and destination planning problem is modeled, transforming it into a non-cooperative game model. This model includes the objective function of each electric vehicle, global coupling constraints, and local constraints. Second, pseudo-gradients are used to transform the game model into a VI problem, introducing edge-based consistency constraints and a heterogeneous step size mechanism. Based on fixed-point iteration and proximal gradient operator theory, a distributed solution algorithm under complete information is proposed. Then, a global estimate of the plans of other users is introduced, proposing a distributed solution algorithm under partial information. This invention avoids the construction of double random matrices through edge-based consistency constraints, and can maintain solution accuracy while meeting low computational requirements when more users participate in the game model.

The application relates to a gradient distribution-based video image denoising method and system, which comprises the following steps: separating each channel of a to-be-processed image; setting a plurality of direction gradient operators, and calculating the gradient values of each direction of a single-channel image current frame; setting a plurality of pixel value screening directions with a current pixel point as the center, judging whether the gradient value difference and the gray value difference between the current pixel point and the neighborhood pixel point meet the screening condition in each pixel value screening direction in turn; if the screening condition is met, the gray value and the pixel number of the neighborhood pixel point are accumulated; if the screening condition is not met, the pixel point screening in the direction is terminated; the average value of the accumulated pixel gray value and the pixel number in each pixel value screening direction is calculated as the filtered gray value of the current pixel point; the gradient distribution-based spatial filtering and the time domain filtering based on adjacent frame difference constraint are combined, so that the real-time video image denoising demand is met.

The invention provides a landslide multi-scale boundary sensing and recognition network based on remote sensing images, and belongs to the technical field of geological disaster prevention. A coder-decoder structure is adopted, a multi-scale cross interaction convolution module is arranged in each stage of a coder, a boundary sensitive refining attention module is arranged in a network bottleneck layer, and self-adaptive cross fusion of multi-branch features is realized through the multi-scale cross interaction convolution module. The boundary sensitive refining attention module is used for realizing dual-path collaborative refining of traditional gradient operator boundary prior and learning boundary, so that the identification recall rate of landslides with different scales and the positioning precision of landslide boundaries are improved at the same time. The technical problems of insufficient multi-scale feature interaction, boundary gradient prior deficiency and large feature fusion information loss in the prior art are solved. All indexes are obviously improved, the overall recognition precision and the boundary positioning precision are higher, and the method has higher practical application value.

The invention relates to the technical field of computer vision and artificial intelligence image processing, and discloses an explosive object target identification method based on multi-dimensional data optimization and feature fusion, and the method comprises the following steps: carrying out the HSV space brightness adjustment, random cutting and gray conversion of an original image, and generating a single-channel gray image; extracting a multi-level feature map by using a convolutional neural network; utilizing a fixed gradient operator to generate a structure attention mask enhanced superficial layer feature map; re-standardizing each feature map to generate a multi-scale feature map with aligned distribution; a bidirectional cross-scale transmission path is constructed and weighted fusion is carried out; and performing category prediction and bounding box regression through the decoupling prediction network, and outputting an explosive object detection result. According to the method, random disturbance and single-channel graying preprocessing logic based on an HSV brightness channel is constructed, non-essential color interference can be actively stripped, a model is forced to focus on inherent geometric texture features of an object, and therefore the illumination invariance basis is established at the data input end.

The application discloses a kind of sheet type porcelain dielectric capacitor appearance defect intelligent detection method and system, the method includes: the acquisition of the measured gray image of measured capacitor, the measured gray image is preprocessed and the region of interest extraction;The outline of measured capacitor is extracted, the center coordinates of the outline are calculated, and the pose of the outline is corrected;The appearance similarity of the measured gray image after correction is judged with standard sample, and the macro defect of measured capacitor is judged, if similarity coefficient is not lower than similarity judgment threshold, then enter next step;Determine the abnormal pixel point in measured image, and the abnormal pixel point is clustered analysis.The application can realize the high-precision automatic detection and classification of capacitor device size compliance, mixing, damage, foreign matter and surface micro scratch by fusing geometric feature constraint, normalized cross-correlation template matching and improved multi-variance differential gradient operator.

The invention relates to the field of vehicle speed measurement, in particular to a vehicle speed measurement method based on videos. The method comprises the following steps: S1, capturing a vehicle driving video by using a camera fixed beside a road; s2, adopting vehicle tracking rules, and selecting different tracking rules according to vehicles of different scenes and sizes; s3, separating a vehicle from the video by using an edge detection algorithm; s4, tracking the vehicles in the continuous frames based on a YOLO model; and S5, calculating the displacement of the vehicle in a specific time period according to the position change of the vehicle in the video, and calculating the speed of the vehicle in combination with the actual length of the road and the moving time of the vehicle in the video. Through an edge detection algorithm and introduction of a Kalman filtering algorithm, more accurate vehicle tracking and speed calculation are realized. Under a complex environment condition, by adjusting the gradient operator, the accuracy of image processing is improved, and vehicle detection and tracking are more accurate and reliable.

The invention discloses a wave field direction calculation method and device based on a stabilized Poynting vector, electronic equipment and a storage medium. The method comprises the following steps: calculating a wave equation forward propagation wave field through a finite difference algorithm based on a migration velocity field and seismic wavelets; calculating a first gradient operator and a first time derivative of a forward propagation wave field of the wave equation; calculating a first Poynting vector of the forward propagation wave field based on the first gradient operator and the first time derivative; calculating a second gradient operator corresponding to the first time derivative and a second time derivative; calculating a second Poynting vector of the time derivative wave field based on the second gradient operator and the second time derivative; and constructing a stabilized Poynting vector of the forward propagation wave field based on the first Poynting vector and the second Poynting vector. According to the method, the calculation instability of the Poynting vector at the local extreme value is effectively eliminated, the efficient calculation of the real propagation direction of the seismic wave field is realized, and the extraction precision of the angle gather is improved.

The invention relates to the technical field of medical treatment, and discloses a telescopic wound endoscope based on AI recognition and a monitoring method.The monitoring method comprises the steps that image data and state data in a wound are collected through a high-resolution endoscope, the image data comprise visible light images and multispectral images, and the state data comprise temperature data and pressure data in the wound; after image data is subjected to noise removal through a Gaussian filtering algorithm, the contour of a wound and the contour of a foreign matter are extracted through an edge detection algorithm, and the edge detection algorithm comprises Canny edge detection and Sobel gradient operators; the image data and the state data are fused to form a temperature heat map and a pressure heat map, and data fusion is carried out through a weighted average method; identifying and classifying foreign matters through an AI model; estimating the three-dimensional position and size of the foreign body based on the contour of the wound and the foreign body; and generating and outputting a report based on the temperature heat map, the pressure heat map, the three-dimensional position and the size. The wound treatment efficiency can be effectively improved, and manual errors are reduced.

The invention discloses a brain source imaging reconstruction method and system based on an ADMM and a diffusion model, and the method comprises the steps: carrying out the estimation of a training source data signal and a real source data signal through a minimum norm estimation method based on a lead matrix, and obtaining a real initial source activity; constructing a sparse gradient operator according to the cortex network topology; determining a denoising mode of the diffusion model through a training demand; and in a denoising mode of the diffusion model, iterative optimization is performed on the source activity and the initial ADMM expansion network through an ADMM algorithm according to the training source data signal, the training initial source activity, the lead matrix and the sparse gradient operator to determine a loss function value, and a target ADMM expansion network is output by taking minimization of the loss function value as a target. Through coupling of physical consistency constraint and lightweight diffusion denoising of the ADMM, the brain source imaging reconstruction precision and calculation efficiency are ensured.

The application discloses a waveform-protected non-source wavelet local scale traveltime inversion method, relates to a non-source wavelet full waveform inversion method established by using a convolution and deconvolution method, and is combined with local scale traveltime inversion, and simultaneously solves the period jump of full waveform inversion and the source wavelet dependence problem. First, the influence of the source wavelet in the observation data and the simulation data is eliminated by using the convolution and deconvolution method, and the original seismic data waveform characteristics are protected. Secondly, local scale traveltime information of the observation data and the simulation data is extracted, and a waveform-protected non-source wavelet local scale traveltime inversion objective function is constructed. Then, the partial derivative of the waveform-protected non-source wavelet local scale traveltime inversion objective function to the velocity parameter is deduced in detail, and the corresponding underground velocity parameter gradient operator of the adjoint state method is deduced. Finally, the stable update of the velocity model parameter is realized by using an L-BFGS optimization algorithm. Finally, the effectiveness of the method is verified by the seismic data test.

The application discloses a physical field gradient calculation method and system based on a graph neural network, and the method comprises the following steps: acquiring physical field data; obtaining an original gradient operator based on a grid topological relation; updating the physical field data based on a graph neural network, obtaining a first gradient operator based on the grid topological relation of the updated physical field data and the original gradient operator; decoupling different directions of the physical field to set corresponding directional training weights; obtaining a second gradient operator based on the original gradient operator, the first gradient operator and the directional training weights; and performing gradient calculation of the physical field based on the second gradient operator. The system corresponds to the method. The application solves the problem that the grid defects in the prior art result in large physical field gradient calculation errors, and improves the precision and robustness of internal force calculation in finite element analysis.