31 results about "Gradient magnitude" patented technology

The embodiment of the invention provides an edge detection method and device based on gradient weighted fusion and an adaptive threshold, and is applied to the field of computer vision and digital image processing. The method comprises the steps of firstly preprocessing an input image, then extracting two groups of gradient magnitude diagrams and directional diagrams through an adaptive morphological operator and a traditional difference operator, and constructing a weighted fusion function according to the gradient direction consistency of each pixel point to obtain a fused gradient magnitude diagram; and adaptively determining a high threshold and a low threshold based on an information entropy maximization principle, executing an improved Canny process by combining the fused gradient magnitude diagram and the second gradient directional diagram to obtain an initial edge diagram, and outputting a final edge detection result after dynamic structure element optimization. In this way, the defects that in a traditional edge detection method, gradient information extraction is not precise, threshold selection lacks adaptability, and an edge result is fractured can be overcome, more robust and more accurate image edge detection is achieved, and the reliability of an edge detection algorithm in a complex image scene is improved.

The invention relates to the technical field of image segmentation, in particular to an anorectal focus automatic segmentation method based on deep learning, which comprises the following steps: acquiring an anorectal image pixel map, extracting contrast and direction offset to mark candidate focus points, screening overlapped marks to generate a focus activation mark map, and establishing a response map to generate a boundary response distribution map. And training the network to output a classification graph, and extracting a truncation path to complete image segmentation. According to the invention, through extracting the contrast value and the gradient amplitude of the local gray level co-occurrence matrix, accurate capturing of the spatial difference of the lesion area under a complex background is realized, through constructing a response map and direction consistency comparison mechanism and combining multi-dimensional features such as a direction gradient histogram and a structure tensor, the area discrimination capability and the edge classification precision are improved, and the accuracy of edge classification is improved. The texture stability is judged by means of anisotropic standard deviation, a fuzzy edge mask is set, truncation paths are screened in combination with a main direction vector included angle deviation trend, and continuity and stability of a boundary convergence position are ensured.

The invention discloses a gradient-guided panchromatic sharpening method for unlocking spatial textures, which comprises two image processing branches: a guide feature extraction branch for capturing gradient features from gradient amplitudes and generating auxiliary gradient features grads and refined guide gradient features Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr and Gsr; gradient features extracted from gradient amplitudes can provide complementary information to promote optimization and improve the overall performance; one branch is a gradient-guided fusion branch, the other branch is a gradient-guided fusion branch, PAN and MSI are combined with extra information of gradient features, and in the fusion branch, a multi-image cross attention module is designed to gradually integrate image features with different spectral bands and resolutions, so that the PAN and MSI fusion process is effectively guided, and the panchromatic sharpening performance is remarkably improved.

This application discloses a method, apparatus, device, and storage medium for edge detection of images, relating to the field of image edge detection technology. The method includes: acquiring an input image and performing structure-preserving filtering on the input image to obtain a smoothed image; calculating a first-order gradient magnitude map based on the smoothed image; generating an adaptive parameter mapping based on the statistical characteristics of the first-order gradient magnitude map; obtaining an edge magnitude map based on the adaptive parameter mapping; determining a global threshold based on the edge magnitude map, and setting a high threshold and a low threshold based on the global threshold; and performing double-threshold hysteresis connection processing on the edge magnitude map based on the high threshold and the low threshold to output a binary edge map. This method can improve the detection accuracy of images.

The invention 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, and the method comprises the steps: obtaining an image of the surface of a brake pad, and calculating the gradient vector of each pixel point; constructing a gradient interaction field based on the intensity of the gradient magnitude and the gradient direction angle, and obtaining the structural significance of each pixel point; path searching is carried out in the local main direction, and linear propagation potential energy of each pixel point is obtained by accumulating structural significance and direction consistency weights of the pixel points on the path; and adaptive threshold segmentation and morphological processing are carried out based on the linear propagation potential energy diagram, and accurate detection of the surface damage area of the brake pad is realized. According to the invention, the technical problems of strong noise interference on the surface of the brake pad and difficulty in detection of discontinuous micro crack characteristics are solved, and the robustness and accuracy of damage detection are improved.

The invention relates to the technical field of tunnel image data processing, in particular to a tunnel smoke image recognition method and system based on multi-scale feature fusion. The method comprises the following steps: obtaining the spatial weight of each pixel point, wherein the spatial weight is in negative correlation with the distance from the pixel point to a vanishing point; obtaining an enhanced gradient of each pixel point, wherein the enhanced gradient is in positive correlation with the closeness degree between the second characteristic value and the first characteristic value of the pixel point, the first-order gradient module value and the spatial weight; taking the enhanced gradient greater than a gradient threshold as a seed point to perform connected domain analysis to obtain a candidate cluster in the tunnel image; according to the method, the extinction coefficient of the candidate cluster where the seed point is located is obtained, the pixel point corresponding to the candidate cluster is obtained from the historical image sequence of the current tunnel image, time domain accumulation is carried out on the enhancement gradient and the extinction coefficient of the pixel point, the smoke judgment score of the current tunnel image is obtained, tunnel smoke is recognized, and the accuracy of the recognition result is effectively improved.

An inventive method for testing the feasibility of acquiring diffusion-weighted measurement data of a test object using a magnetic resonance system with a measurement protocol with non-trapezoidal gradient pulse shapes for diffusion coding comprises the steps a) Loading a non-trapezoidal gradient pulse shape, b) Loading pre-prepared characteristics for the loaded non-trapezoidal gradient pulse shape, c) Receiving a condition that must be met during the execution of the acquisition of the diffusion-weighted measurement data, d) Determine at least one gradient amplitude relevant for the gradient pulse shape based on the charged characteristics and the condition, e) Checking the feasibility of acquiring diffusion-weighted measurement data of the object under investigation using the magnetic resonance system with a measurement protocol with the non-trapezoidal gradient pulse shape based on at least one specific relevant gradient amplitude.

The invention discloses a semi-supervised medical image segmentation method and system, and relates to the technical field of image segmentation, and the method comprises the steps: calculating the spatial average weighted sum of the three-dimensional gradient magnitude and a discrete three-dimensional Laplacian operator through a foreground probability graph outputted by a student network, and obtaining a global boundary roughness scalar; the voxel-level entropy and the soft boundary indicator are combined to construct the consistency loss of roughness perception, and adaptive course learning is realized; carrying out residual modulation on geometric contrast loss by driving a scaling factor, and enhancing the structural discrimination force of a boundary region; boundary gradient alignment, curvature smoothing and a pseudo-supervision threshold are guided, and the student network and the teacher network are optimized through weighted joint of the three so as to perform index moving average updating. According to the method, the global boundary roughness scalar is introduced, entropy consistency, geometric boundary comparison and gradient-curvature-pseudo supervision joint regularization are cooperatively regulated, and the boundary precision and robustness of medical image segmentation are remarkably improved under the semi-supervised condition.

This invention relates to the field of image recognition technology, specifically to an image recognition method and system for precision weld formation quality, comprising the following steps: S1: acquiring an original grayscale image containing the weld region; S2: calculating an adaptive segmentation threshold and extracting the weld target region accordingly; S3: performing Sobel convolution on the weld target region to generate a gradient magnitude image; S4: fusing the convolution results at various scales to generate an enhanced gradient image; S5: extracting closed contours from the enhanced gradient image and calculating the Euler number, number of holes, and rate of change of boundary curvature for each contour; S6: outputting the judgment result of weld formation quality defects based on combined features. This invention, through the collaborative design of synchronous triggering of welding current, high-order gradient enhancement, and topological feature judgment, achieves high-precision automation of weld image acquisition and quality defect recognition.

The invention relates to the technical field of temperature monitoring, in particular to a temperature monitoring method and system for a reaction kettle based on machine learning. The method comprises the following steps: acquiring infrared thermal images of the outer wall of the reaction kettle at multiple moments; calculating the weighted gradient magnitude of the image at the current moment, and inhibiting the gradient of the background region based on the entropy value of the image at the previous moment to obtain a corrected gradient map; determining a threshold value for screening the key pixel points based on historical image changes; extracting global gradient features from the corrected gradient map, and screening key pixel points to extract local focus features; splicing the global and local features into a fusion feature vector; and inputting the fusion feature vectors at the current moment and the last moment into a machine learning model to obtain the current temperature state of the reaction kettle. According to the scheme of the invention, noise interference can be suppressed, multi-dimensional features are fused, judgment is carried out by using a temperature change trend, and the monitoring reliability is improved.

This invention provides a method for generating a seabed DEM based on large-scale nautical chart depth points and high-resolution remote sensing surfaces, belonging to the field of marine surveying technology. The method includes: acquiring discrete depth points from remote sensing images and nautical charts; extracting a relative depth trend surface based on a dual-band logarithmic ratio model; calculating the image gradient field and pixel-level structure tensor, solving for the principal eigenvectors, and constructing an anisotropic diffusion tensor by combining local gradient magnitudes; using the nautical chart depth points to perform regression calibration on the trend surface to generate sparse residuals, and establishing a residual calculation grid; using the anisotropic diffusion tensor as a guiding constraint, performing non-uniform diffusion calculation on the sparse residuals to generate a global continuous residual correction surface; superimposing the correction surface onto the trend surface, and obtaining the final seabed DEM through anomaly removal and edge smoothing. This invention solves the problems of low accuracy and loss of detail in complex shallow sea topography inversion.

The invention provides a low-dose CT (Computed Tomography) image denoising method fusing gradient extraction and mixed loss. Comprising the following steps: S1, acquiring a low-dose CT image as input, performing numerical truncation and normalization preprocessing on input data, and constructing a de-noising model based on a U-Net backbone network; s2, constructing a gradient extraction module at the output end of the U-Net backbone network in parallel, extracting high-frequency edge features of the image in horizontal and vertical directions by using a Sobel operator, and mapping the image from an intensity domain to a gradient magnitude domain; s3, constructing a mixed loss function including pixel consistency loss, structural similarity loss and gradient perception loss; and S4, calculating the difference between the predicted image and the gold standard image by using the mixed loss function, and optimizing network parameters through back propagation to obtain a denoised CT image. According to the method, competition conflicts between mean square errors and structure indexes can be effectively relieved, and edge artifacts of a high-density skeleton region are eliminated while noise is suppressed.

This invention relates to the field of tunnel image data processing technology, and particularly to a method and system for tunnel smoke image recognition based on multi-scale feature fusion. The method includes the following steps: obtaining the spatial weight of each pixel, where the spatial weight is negatively correlated with the distance from the pixel to the vanishing point; obtaining the enhancement gradient of each pixel, where the enhancement gradient is positively correlated with the proximity between the second and first feature values ​​of the pixel, the first-order gradient magnitude, and the spatial weight; using enhancement gradients greater than a gradient threshold as seed points for connected component analysis to obtain candidate clusters in the tunnel image; obtaining the extinction coefficient of the candidate cluster where the seed point is located; obtaining the pixel corresponding to the candidate cluster in the historical image sequence of the current tunnel image; and performing temporal accumulation on the enhancement gradient and extinction coefficient of the pixel to obtain the smoke determination score of the current tunnel image, thereby identifying tunnel smoke and effectively improving the accuracy of the recognition results.

This invention relates to the field of industrial vision technology, specifically to a deep learning-based method for detecting bubble defects in rubber bonding. The method includes the following steps: acquiring a grayscale image of the rubber bonding surface; calculating the gradient magnitude of the image pixels; obtaining a gradient magnitude distribution map; performing iterative calculations on the gradient magnitude distribution map; smoothing background noise on the rubber surface while simultaneously sharpening the edges; and generating an anisotropic diffusion-enhanced image. In this invention, a set of gradient vectors and position vectors is constructed based on candidate regions. The overall directionality of the gradient is quantified by calculating the centripetal convergence index of the vector field. The unique optical morphological characteristics of bubbles are used to distinguish true defects from surface stains or planar texture differences at the physical property level, eliminating false detections caused by texture interference. This achieves high-precision bubble defect localization and morphological confirmation without requiring extensive pixel-level annotation.

This invention belongs to the field of image processing technology, specifically relating to a method and system for detecting the wear state of rotary drilling bits based on image recognition. The method includes: acquiring scalar brightness field and global potential flow gradient magnitude field data of the drill bit surface image; calculating the local potential well smoothing index and potential flow divergence coefficient based on the scalar brightness and potential flow gradient magnitude within a local window of each pixel; performing a top-hat transformation on the scalar brightness field to obtain the entity protrusion weight of each pixel, and weighting it with the local potential well smoothing index and potential flow divergence coefficient to obtain the degradation retention weight; mapping all pixels to graph network nodes, calculating the walk transition probability of a node moving to any of its neighboring nodes to construct a global graph transition probability matrix; performing PageRank iteration based on the transition probability matrix until convergence to obtain the retention probability of each pixel, thereby determining the wear state of the drill bit. This invention effectively suppresses mud interference and improves detection accuracy.

This invention discloses a computer vision-based method and system for analyzing surface defects in electrical porcelain blanks. The method includes: comparing each image in a historical image sequence with a standard template image; determining the interference region image corresponding to each image based on the covariance eigenvalue and gradient magnitude of the gray-level gradient matrix; acquiring a real-time image; filtering out the target historical image most similar to the real-time image through multi-scale feature matching; mapping the interference region of the target historical image to the real-time image through feature point registration; dynamically adjusting the expansion coefficient according to the local gray-level variance of the mapped region; predicting the number of pixels to be removed and generating a circular region mask; obtaining the image to be analyzed after removing interference; extracting candidate defect regions in the image to be analyzed through local contrast mapping and graph cut segmentation to obtain the defect type and parameters. This invention effectively eliminates process interference and significantly improves the accuracy and robustness of surface defect detection in electrical porcelain blanks.

This invention discloses a method for fusing InSAR and GNSS heterogeneous surface deformation based on an adaptive sparse spatial basis, belonging to the field of geological disaster monitoring and data processing technology. The method first acquires and aligns InSAR and GNSS deformation sequences; calculates the InSAR deformation feature field and its spatial gradient magnitude; sparsifies control points according to gradient thresholds to construct an adaptive sparse spatial basis set that is sparse in low-gradient regions and dense in high-gradient regions; models the deformation observations, combines the spatial basis set with the state transition equation, and uses filtering smoothing and the EM algorithm for iterative solution to reconstruct a high spatiotemporal resolution deformation sequence. This invention can adaptively match deformation spatial features, reducing the computational dimensionality while preserving local deformation details, and achieving high-precision fusion of heterogeneous data.

The invention relates to a flaw recognition system for a special-shaped sealing bolt forming process based on image processing, and belongs to the technical field of image recognition. The system considers that a complex structure on the surface of the special-shaped sealing bolt enables the gray gradient magnitude difference of different angular points to be large, abandons a traditional angular point judgment method for comparing the gradient magnitude with a threshold value, and determines the angular point degree according to the multi-dimensional features of a target pixel point and a neighborhood pixel point about the distance, the gradient vector difference and the gradient magnitude of the target pixel point and the neighborhood pixel point. All the pixel points are clustered in combination with the gray values and the angular point degrees of the pixel points, and the error degree caused by local too dark influence of the collected image when the angular point degrees of the pixel points in the class clusters are determined is determined according to the gray values, the sizes and the angular point degree mean values of all the classes of clusters; and on the basis of the error degree, combining the outlier degree of the target pixel point in the class cluster to determine an angular point degree correction coefficient, and completing angular point judgment. According to the method, the angular point judgment accuracy of the special-shaped sealing bolt can be remarkably improved, so that the defect recognition accuracy is improved.

The invention belongs to the technical field of image processing, and particularly relates to a rotary excavating drill bit abrasion state detection method and system based on image recognition, and the method comprises the steps: obtaining the scalar brightness field and overall situation flow gradient amplitude field data of a drill bit surface image; calculating a local potential well gentle index and a potential flow divergence coefficient based on the scalar brightness and the potential flow gradient amplitude in the local window of the pixel point; performing top-hat transformation on the scalar brightness field to obtain an entity bulge weight of a pixel point, and weighting by combining a local potential well gentle index and a potential flow divergence coefficient to obtain a degradation retention weight; mapping all pixel points into graph network nodes, and calculating the migration transition probability of the nodes transferred to any adjacent node so as to construct a whole graph transition probability matrix; and PageRank iteration is executed based on the transition probability matrix until convergence, and the retention probability of the pixel points is obtained, so that the wear state of the drill bit is judged. According to the invention, mud interference is effectively inhibited, and the detection accuracy is improved.

The application relates to the technical field of image processing, in particular to a bearing inner and outer ring synchronous flaw detection method and system, which comprises the following steps: determining a micro crack evaluation index based on the distribution of the gradient direction angles of all pixel points in each local region and the variation characteristics of the pixel values of all pixel points in different gradient directions; determining a pit evaluation index of each bearing image based on the confusion degree of the pixel values of all pixel points in each local region, the distribution of the gradient direction angles of all pixel points, the brightness distribution of the pixel values of all pixel points and the discrete degree of the gradient amplitudes; and combining the micro crack evaluation index to determine the bearing spalling damage confidence of each bearing image, so that the bearing inner and outer ring spalling damage is determined. The application fuses the multi-dimensional characteristics of micro cracks and small pits, and adaptively sets a determination threshold, thereby improving the accuracy and reliability of bearing spalling damage detection.

A method and apparatus for video coding are disclosed. According to the method, bilateral filter options are determined in a slice level, wherein the bilateral filter options are determined by a syntax or implicitly decided according to coding information of coding unit (CU) or transform unit (TU), and wherein the coding information of CU or TU includes a transform block (TB) size, a quantization parameter (QP), a width / height aspect ratio, a neighbouring mode, at least one of variance, mean value and gradient magnitude from a predictor of the TB, or a combination thereof. Bilateral filtering is applied to the input data according to the bilateral filter options to derive a set of bilateral filter offsets, wherein the input data satisfies a predefined combination of prediction mode and CBF (Coded Block Flag) value. A target reconstructed sample is adjusted using one or more offsets including the set of bilateral filter offsets.

The invention relates to the technical field of image processing, in particular to a synchronous flaw detection method and system for an inner ring and an outer ring of a bearing, and the method comprises the following steps: determining a microcrack evaluation index based on the distribution of gradient direction angles of all pixel points in each local area and the change characteristics of pixel values of all pixel points in different gradient directions; based on the confusion degree of the pixel values of all the pixel points in each local area, the distribution of the gradient direction angles of all the pixel points, the brightness distribution of the pixel values of all the pixel points and the dispersion degree of the gradient amplitudes, the pit-shaped evaluation index of each bearing image is determined, and the micro-crack evaluation index is combined to obtain the micro-crack evaluation index of each bearing image. And determining the bearing peeling damage confidence coefficient of each bearing image so as to perform peeling damage judgment on the inner and outer rings of the bearing. According to the method, the accuracy and the reliability of bearing peeling damage detection are improved by fusing the multi-dimensional characteristics of the microcracks and the small pits and adaptively setting the judgment threshold.

The invention relates to the technical field of automobile rubber tube defect positioning, and discloses a rubber tube defect positioning method for improving a sparrow algorithm and optimizing a neural network, which comprises the following steps of: acquiring a rubber tube surface expansion image, and performing Gaussian filtering denoising on the expansion image to obtain a preprocessed expansion image I (x, y); calculating Sobel gradient magnitude G (x, y) and Laplacian curvature response delta 2I (x, y) of the pre-processed expanded image I (x, y); according to the defect positioning method, a physical sensing defect response objective function fusing gradient amplitude and Laplace curvature response is constructed, so that the algorithm can capture edge change characteristics and curvature characteristics of the surface of the rubber tube at the same time, the sensing capability for various low-contrast defects is remarkably improved, real defects and normal textures are effectively distinguished, and the defect positioning accuracy is improved. And the false detection rate and the omission ratio are greatly reduced.

This invention discloses a CNC milling method for creating anisotropic frictional surfaces. Through milling simulation under various tool attitude parameters, the method involves meshing surface data, extracting contact areas, calculating surface gradients and gradient magnitudes, extracting gradient magnitude sets of forward and reverse sliding contact leading edges, calculating forward and reverse sliding friction coefficients, and calculating anisotropic evaluation indices. A heatmap of the anisotropic evaluation indices is then generated, and sensitive directional ridges are created, enabling quantitative prediction and characterization of surface friction behavior. This invention, starting from the anisotropic requirements of the surface microstructure, intelligently reverse-engineers tool attitude parameters based on set cutting parameters. Milling is then performed according to the recommended parameter combinations and set cutting parameters to obtain viscoelastic material workpieces that meet the requirements for directional friction or sealing functions. This invention is of great significance for meeting the requirements of directional friction and sealing functions in sealing pairs.

This application provides a method for detecting a door gap of a vehicle body based on edge detection, including: detecting a vehicle body door gap image; performing image filtering; smoothing a filtered image, and calculating gradients and directions of the image to form a gradient image; performing non-maximum suppression on gradient magnitudes, and only retaining local maxima to obtain discrete points of a gradient edge contour image; giving a low threshold and a high threshold, marking points with gradient values greater than the high threshold as strong edge pixels, and anchor points in the image, and connecting the anchor points to form an edge image; and extracting a feature point of the edge image, matching the feature point with a laser-marked image window, finding a position perpendicular to the door gap, and calculating a Manhattan distance at the window to obtain a minimum for a width of the door gap.

The invention discloses an arbitrary-scale super-resolution reconstruction method for a multi-source heterogeneous DEM, and the method comprises the steps: carrying out the down-sampling of an input image, obtaining three down-sampling images of different sizes, carrying out the processing of a reconstruction network model in a unified manner, obtaining three initial reconstruction images, and carrying out the up-sampling operation in a unified manner, and obtaining three final reconstruction images; the reconstruction network model is constructed and reconstructed based on a meta collaborative learning method and comprises three branches, each branch comprises a plurality of module groups which are connected in sequence, each module group comprises a feature extraction module and a feature adaptive module which are connected in sequence, and parameters of the feature extraction modules at corresponding positions in the three branches are shared; the feature extraction module is used for obtaining a local feature map and a global feature map of the input image, and performing fusion processing on the local feature map, the global feature map and the input image according to the gradient magnitude of the input image to obtain a final feature map; and the feature self-adaptive module is used for carrying out self-adaptive adjustment on the final feature map.

This invention relates to the field of automated inspection technology and discloses a method for identifying surface defects in steel grating welds based on industrial vision. The method includes: acquiring an initial grayscale image of the steel grating weld and performing standardization processing; calculating local spatial contrast to generate a binary weld mask; calculating the orthogonal gradient magnitude and spatial phase angle of the image; calculating the sum of double phase angle projections within a local window to obtain a local gradient direction consistency index; using the consistency index to exponentially amplify the gradient magnitude, and combining it with the mask to obtain a magnified defect feature map; constructing a dynamic reference surface based on the feature map and deriving dynamic judgment coefficients to extract the defect state matrix; finally, multiplying the defect state matrix with the corresponding gradient magnitudes and summing them globally to output a defect severity score and compare it with a judgment threshold.

The invention discloses a demosaicing method and system for a multispectral image, relates to the field of spectral image processing, and solves the problems that an existing multispectral demosaicing technology lacks high-frequency details, easily generates artifacts, is seriously limited by a high-quality data set, and is high in calculation complexity and low in spectral accuracy. Collecting a multispectral image to be processed; performing bilinear interpolation on the spectral image of each channel to obtain a corresponding interpolation spectral image and an intensity component; obtaining a corresponding injection gain based on the intensity component; processing the multispectral image through a customized filter to obtain a pseudo panchromatic image; obtaining a gradient amplitude through a Sobel filter based on the pseudo panchromatic image; and based on the intensity component, the pseudo panchromatic image and the gradient magnitude, obtaining a mosaic spectral image through an improved component replacement method. The method is suitable for the fields of industry, scientific research and the like, and a new solution is provided for related industries.

This invention discloses a machine vision method, system, storage medium, and device for measuring angle bisectors. The method includes: acquiring an image, a region of interest (ROI) of the angle to be measured, and an initial parameter vector; calculating the gradient magnitude and direction of the image within the ROI; constructing an energy function based on the gradient magnitude, the direction, and the current parameter vector to obtain an energy function value and a residual vector; iteratively optimizing the energy function based on the energy function value and the initial parameter vector to obtain target parameters; and performing visualization output based on the target parameters, wherein the output includes a visualized image with angle bisector annotations. This invention achieves high-precision and robust visual measurement of angle bisectors through a global energy minimization framework. It does not rely on precise local feature positioning and can stably output sub-pixel-level results even under noisy, blurred, or occluded conditions. It is applicable to various angles and significantly improves automation and industrial adaptability.

This invention belongs to the field of image recognition technology, specifically relating to a control method and system for a die-cutting machine based on image processing. The method includes: acquiring an image to be detected; performing feature reconstruction using an anisotropic convolution template to output an enhanced image with marker points; extracting a feature extraction region; determining the texture energy gradient value based on the gradient magnitude of the target pixel and the grayscale standard deviation within the local window region; acquiring a reference ideal template; calculating the displacement vector magnitude using optical flow; and determining the deformation entropy coefficient of the feature extraction region based on the texture energy gradient value; generating a servo compensation factor based on the deformation entropy coefficient, its rate of change, and the instantaneous linear velocity of the die-cutting process, and converting this factor into a control signal to adjust the rotation phase of the die-cutting tool. This invention can sense micro-stress diffusion and actively counteract mechanical response hysteresis, improving the dynamic alignment accuracy of high-speed die-cutting.