35 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 a background schlieren method flow field flow velocity measurement method based on pseudo-schlieren images. The method comprises the following steps: constructing a background schlieren experiment measurement system; obtaining a speckle plate image sequence without a flow field background; obtaining a background distortion image sequence; obtaining a pseudo schlieren image sequence; and obtaining the final flow velocity of the flow field to be measured. The method has the advantages that a background plate distortion image sequence under the action of a flow field is collected through a high-speed camera, and pseudo-schlieren transformation processing is carried out through an improved mixed gradient operator and a multi-scale feature fusion technology; and a flow velocity field is solved in combination with a PIV-optical flow fusion algorithm, and real-time processing is realized through GPU parallel computing. The method breaks through the limitation that the traditional PIV technology depends on tracer particles, has the advantages of non-contact, full-field measurement, high temporal-spatial resolution and the like, and is particularly suitable for compressible flow field and turbulent flow field measurement. Experiments show that the method can realize high-resolution and high-frame-rate image processing, the uncertainty of flow velocity measurement is less than 3.2%, and a new technical means is provided for complex flow field diagnosis.

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 invention provides a numerical control machining tool wear multispectral imaging detection method and system. The method comprises the steps that a three-dimensional data cube is constructed through a multispectral image sequence of a tool machining area; defining a spectrum detection window of each pixel point in the three-dimensional data cube, dividing the spectrum detection window into a main detection sub-block and auxiliary detection sub-blocks, and generating a spectrum difference feature map based on a difference relationship between a spectrum curve of each pixel in the main detection sub-block and an average spectrum curve of all the auxiliary detection sub-blocks; determining a projection vector according to a target spectral feature in the center detection sub-block and a background spectral feature in the local detection window; and determining a spectral feature map according to the projection vector and the three-dimensional data cube, and recognizing an abnormal wear area in the tool machining area according to a multidirectional gradient feature map constructed by a spatial gradient operator corresponding to each pixel in the spectral feature map and a spectral difference feature map. According to the technical scheme provided by the invention, the subtle difference between the wear region and the background region can be distinguished in a multi-dimensional interference state.

The invention relates to the technical field of target detection, in particular to a blueberry fruit focusing detection method and system based on a lightweight YOLO model, and the method comprises the steps: obtaining blueberry images to form a multi-dimensional data set, and carrying out the marking of the obtained multi-dimensional data set; performing data enhancement based on the acquired multi-dimensional data set, including performing geometric transformation on the acquired original data set, and performing optical simulation and occlusion simulation by adding Gaussian noise and gradient operators; and constructing a detection model by taking the preprocessed multi-dimensional data set as input, introducing a dynamic attention mechanism on the basis of the detection model, carrying out model training and optimization on the basis of the constructed detection model, and inputting a test set for detection by utilizing an optimal weight obtained by training to generate a final detection result. According to the method, dual optimization of semantic understanding and accurate positioning is realized through feature fusion, and the detection precision of overlapped fruits and small targets is effectively improved.

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 kind of medical image cardiothoracic ratio measurement method and system based on artificial intelligence, method includes: obtaining the first picture that user stands in front of radiographic mainboard and is photographed by top camera, according to first picture, obtain the initial azimuth of user relative to radiographic mainboard;Judge whether the absolute value of initial azimuth exceeds first preset angle;Start chest radiography image device, collect the first chest medical image of user standing in front of radiographic mainboard;According to initial azimuth adjustment binary threshold, according to binary threshold to first chest medical image is carried out binary processing and obtains binary image;Gradient operator is used to extract the contour line of lung in binary image;The cardiothoracic ratio obtained by calculating heart transverse diameter divided by thoracic transverse diameter.The binary threshold of lung edge is adjusted in the application to improve the position accuracy of lung contour line, and then accurate cardiothoracic ratio is obtained.

The application discloses a U-Net-based optical and SAR remote sensing image optical flow registration method and relates to the technical field of heterogeneous image registration, and comprises the construction of an image data set, image preprocessing and the labeling of a region of interest; two U-Net network models are used to train an optical remote sensing image segmentation model and a SAR remote sensing image segmentation model respectively; U-Net network segmentation results of a to-be-registered image pair are acquired, and pixel point sets marked in specified channels of two segmentation images are recorded respectively; gradient operators are used to construct class GLOH descriptors of region-of-interest feature points and pixel points in specified neighborhoods of the region-of-interest feature points in the to-be-registered image pair; and the region-of-interest feature points in the to-be-registered image pair are registered by using a Gaussian pyramid LK optical flow method. The application can realize the registration of heterogeneous images with higher precision, stronger purpose and better timeliness.

The invention relates to the technical field of ultrasonic tomography, and discloses a three-dimensional ultrasonic tomography sound velocity imaging method based on a multi-template fast marching method, which comprises the following steps: acquiring parameters of a three-dimensional ultrasonic tomography system and initial sound velocity distribution of biological tissues, and establishing a curved ray model based on an eikonal equation; discretizing the propagation time gradient in the eikonal equation by adopting second-order finite difference approximation, and covering 18 neighborhoods of the grid by adopting templates in four directions to carry out combined solution, so as to obtain ultrasonic propagation time distribution of the whole grid; and iteratively reconstructing a three-dimensional sound velocity distribution image through a maximum posterior probability estimation algorithm based on the ultrasonic propagation time distribution. According to the method, the propagation time gradient in the second-order finite difference approximation eikonal equation is adopted to reduce discretization errors, a plurality of direction templates are used, more accurate difference approximation is carried out on gradient operators, and the non-orthogonal propagation path of the ultrasonic waves in the three-dimensional space is completely captured.

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 discloses a physical field gradient calculation method and system based on a graph neural network. The method comprises the following steps: acquiring physical field data; obtaining an original gradient operator based on the grid topological relation; updating the physical field data based on the graph neural network, and 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 direction training weights; obtaining a second gradient operator based on the original gradient operator, the first gradient operator and the direction training weight; and performing gradient calculation of the physical field based on the second gradient operator. The system corresponds to the method. According to the method, the problem of large physical field gradient calculation error caused by grid defects in the prior art is solved, and the precision and robustness of internal force calculation in finite element analysis are improved.

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 application provides a multi-focus image fusion method combining a deep residual network and a variational method, and relates to the technical field of digital image processing. The application firstly acquires a first source image and a second source image which are complementary to a focusing area, determines a first-order gradient and a first-order gradient module of the first source image and the second source image; outputs a focus point image and a gradient module score image through an MResNet deep residual network after training, generates an initial fusion image and a fusion gradient guide term; constructs a light-weighted total variation model which only adopts a two-direction first-order gradient operator, and obtains a total variation fusion result at a junction; and performs block fusion based on a weight map to obtain a final fusion image. The application can retain clear area information of source images and improve the transition continuity at a focusing and defocusing junction.

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 present invention relates to the field of infrared image processing technology, and more particularly to a method for correcting infrared image non-uniformity vignetting based on gradient priors. The method comprises obtaining a uniformly radiated infrared noise image of a target imaging system, obtaining a gradient trend of infrared non-uniformity vignetting based on the infrared noise image; obtaining a real-time infrared image of the target imaging system, obtaining a real-time gradient operator and a real-time gradient trend of the real-time infrared image based on the gradient trend; constructing an objective function based on the gradient trend, the real-time gradient operator, and the real-time gradient trend, solving the objective function to obtain an optimal value of the real-time gradient operator; obtaining an optimal real-time gradient trend based on the optimal value of the real-time gradient operator, and correcting the real-time infrared image using the optimal real-time gradient trend to obtain a target infrared image after non-uniform vignetting correction. The method of the present invention can better achieve correction of infrared non-uniformity vignetting.

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 relates to the technical field of image quality inspection, in particular to a casting surface defect segmentation method and quality inspection system based on multi-modal attention, and the method comprises the steps: collecting a color image and a depth image for a to-be-detected surface, and then sending the color image and the depth image into a casting defect recognition model; the casting recognition model is of a codec structure, color image features and depth image features are extracted respectively, fused and then subjected to up-sampling, and then a segmentation result corresponding to casting surface defects is obtained; for a depth image, a plurality of gradient operators with different scales are adopted to perform enhancement processing in an image feature extraction process. Aiming at the defects of different scales such as cracks, pores and large-area recesses on the surface of the casting, a plurality of gradient operators with different scales are designed to enhance the part of the depth image, so that the defects with different sizes can be effectively sensed in the extraction process of the depth image; and the method has better robustness for noise, illumination variation and tiny irregularity of the surface of the casting.

A machine vision detection method for wear of a rear tool face of a micro-milling tool comprises the following steps: firstly, acquiring an original micro-tool wear image, and then performing image enhancement preprocessing on the original micro-tool wear image by using a Canny edge detection operator to obtain an enhanced image; the method comprises the following steps: firstly, performing image segmentation on an enhanced image through a Canny gradient operator to obtain a tool boundary, and then identifying the tool boundary to obtain matrix data; firstly, angular point feature detection is carried out on matrix data to obtain AKAZE feature points of an image, then the position relation of the AKAZE feature points is extracted to complete image registration, and a registered image is obtained; the method comprises the following steps: firstly, calculating according to a registered image to obtain a corrected cutter wear region, then correcting the cutter wear region by using Hough transformation, and then determining cutter flank wear parameters according to scale invariance, thereby completing cutter flank wear machine vision detection. According to the invention, the efficiency is high when multiple groups of samples are detected.

The invention discloses a road detection method, device and equipment, a medium and a vehicle, and the method comprises the steps: obtaining a target image; performing gradient extraction on the target image by using a preset edge gradient operator to obtain a gradient image; determining a detection result of the road line based on the gradient image; wherein the gradient direction of the edge gradient operator corresponds to the extension direction of the road line in the target image. On the basis of keeping high precision and high robustness of road detection, dependence on a large-scale neural network model and a complex feature extraction algorithm can be avoided, and dependence on computing resources is reduced; and the system can adapt to various complex road conditions and unstructured road scenes, and the adaptability of the system to various road forms such as curves and widened lanes is remarkably improved.

The application discloses a kind of based on deep learning's sleep spindle automatic detection method, system and medium, the method constructs U type neural network, embeds multi-stage edge guide attention module in coding and decoding stage, utilizes shallow gradient operator to capture spindle start-stop boundary mutation, utilizes deep layer smooth operator to extract envelope contour, explicit injection morphology priori;Recursion generalization self-attention module is introduced at the place of jump connection, and global context is constructed by recursion down sampling and is calibrated local feature;Particularly, design quality evaluation network to generate quality factor, based on the gating fusion mechanism of quality factor, the enhanced features are limited incremental fusion, to prevent noise interference and distribution drift.The present application effectively solves the problem that the existing model is insufficient for spindle shape feature, low signal-to-noise ratio, poor robustness and weak multi-scale adaptability, significantly improves the boundary positioning accuracy and cross-subject generalization ability of sleep spindle detection.

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 invention relates to an electronic paper display enhancement method based on visual perception and dynamic prior information, and belongs to the technical field of display. The method comprises the following steps: firstly, quantizing electronic paper refreshing delay characteristics through a three-order gradient operator to generate display distortion priori, and dynamically predicting contrast enhancement parameters by using a convolutional network; secondly, a dual-channel unsupervised enhancement network is constructed, UNet-based ghosting suppression and anti-gamma transformation detail enhancement are performed respectively, and output is performed through adaptive weight fusion; and finally, designing a dynamic error diffusion system driven by visual perception, combining a frequency domain weighted filter and a spatial domain dynamic quantization threshold matrix, optimizing a refreshing sequence by adopting a snakelike scanning path, coding a processing result into a 16-order driving waveform, and controlling adaptive electronic paper hardware display through dynamic voltage. According to the method, the problems of gray scale distortion, edge blurring and ghosting effect of the high-dynamic-range image in electronic paper display are effectively solved, and the detail reduction degree and the visual comfort degree are remarkably improved.

A video coding intra-prediction method is provided. The method includes: acquiring a reference area of a current block; determining a size or a type of a gradient operator; estimating horizontal gradients and vertical gradients of samples within the reference area with the gradient operator; and selecting an intra prediction mode or updating a list of candidate prediction modes for the current block based on the horizontal gradients and the vertical gradients.