47 results about "Structure tensor" patented technology

The present invention discloses a method and system for blur removal and jitter correction in visual monitoring of super high-rise buildings, comprising the following steps: a visual sensor acquires a video stream at a fixed frame rate, and locates an initial region of interest (ROI) by mapping prior information; the spatial gradient field is calculated for the ROI image of the current frame, and a Gaussian-weighted two-dimensional gradient structure tensor matrix is ​​constructed; for the clear image sequence that passes blur detection, variational mode decomposition is then used in the time domain to decouple the displacement time series into eigenmode functions of different frequencies, and the low-frequency components are reconstructed to preserve the true deformation of the building; the reconstructed low-frequency displacement signal is output as the final monitoring result. The present invention employs a lightweight algorithm design throughout, significantly reducing the matrix operation dimension of subsequent processing through a dynamic ROI clipping mechanism; the structural tensor eigenvalues ​​are solved using a direct algebraic analytical method, avoiding complex matrix iterative decomposition.

The application provides a kind of multi-modal feature fusion-based harsh environment image enhancement and restoration method, belongs to image enhancement processing field;The method first obtains multi-modal image and calculates each modal degradation degree graph;Based on the degradation degree and gradient amplitude, a fusion weight is constructed, and the local dominant direction and coherence are obtained by structure tensor analysis;According to the coherence, the pixels are partitioned, and the adaptive processing of each modal gradient is carried out by using hard projection, isotropic fusion or soft projection strategy respectively;Based on the fusion weight, the processed gradient is weighted and fused, and the fusion gradient field is obtained combined with the enhancement factor g (x) ;Finally, an energy function containing pixel fidelity term and gradient fidelity term is constructed, and the enhanced fusion image is reconstructed by weighted least squares optimization;The application suppresses the contribution of blurred modal through degradation perception mechanism, and solves the gradient direction conflict through structure adaptive projection, which significantly improves the clarity and structure fidelity of multi-modal image fusion in harsh environment.

The application discloses an infrared image denoising method, computer equipment, medium and product, and relates to the technical field of image processing. The method comprises the following steps: firstly, constructing a structure tensor matrix corresponding to each pixel point in the infrared image through horizontal and vertical pixel gradient matrices; then, realizing separation of a base frequency component and a high frequency component of the infrared image based on an anisotropy measurement map, processing the main structure and the detail information respectively, and avoiding problems such as detail loss or noise residue caused by the traditional denoising method. In the high frequency component processing, the high frequency noise is efficiently filtered through the steps of overlapping block division, structure similarity reference sub-block screening, three-dimensional block group singular value decomposition and adaptive threshold shrinkage, and the integrity of the detail information is ensured by means of sub-block stacking and inverse transformation reconstruction. Finally, the fusion of the base frequency component image and the denoised high frequency component image ensures that the output image has clear main structure and retains key details such as edges and textures.

This invention relates to the field of image analysis technology, specifically a deep learning-based method for detecting surface defects in tab adapters. The method includes acquiring the three-dimensional geometric curvature parameters of the bending region through multi-view polarization imaging; decoupling specular reflection and diffuse structural features using a photometric distribution model to resolve high-brightness reflection interference; nonlinearly projecting the surface texture onto a two-dimensional plane using a local tangent space mapping matrix to generate an isotropic corrected texture map to compensate for geometric distortion; identifying suspected defect points that disrupt flow continuity through singular value decomposition of the anisotropic structure tensor; and introducing stress distribution priors and epipolar geometric constraints for multi-view spatial consistency verification to effectively filter lighting artifacts. The verified region and corrected texture are fused and input into a deep recognition network with a spatial attention mechanism to analyze the surface defect image. This invention achieves accurate analysis of surface defect images through deep coupling of three-dimensional geometry and a photometric physical model.

The application relates to the technical field of medical ultrasonic imaging and digital image processing, in particular to an ultrasonic elasticity image denoising and enhancing method, which comprises the following steps: acquiring original ultrasonic elasticity imaging data to be processed, and constructing a structure tensor matrix reflecting local texture directions; solving the anisotropic coherence degree of each pixel point, obtaining a structure attribute discrimination result, and constructing a structure confidence atlas; collecting the point spread function characteristics of an ultrasonic imaging system, establishing a speckle noise statistical model; obtaining a local smoothing coefficient of the first iteration, and performing anisotropic diffusion correction on an initial elasticity distribution matrix to obtain a first modified elasticity image matrix, and updating the structure confidence atlas to obtain a final denoised and enhanced target elasticity image; the application effectively suppresses multiplicative granular speckles, eliminates the ladder effect or artifacts easily generated by conventional methods, and significantly improves the image signal-to-noise ratio.

The present application relates to the technical field of image enhancement, in particular to a contrast enhancement method for nylon thread surface microscopic image, comprising the following steps: obtaining a nylon thread surface microscopic gray image, calculating the horizontal and vertical gradient values of the pixel points of the nylon thread surface microscopic gray image, constructing a pixel-level structure tensor matrix, performing eigenvalue decomposition operation on the pixel-level structure tensor matrix, and generating a nylon thread texture coherence distribution factor. In the present application, the nylon thread roughness gain mapping points are multiplied by the detail component and reconstructed with the background component to output a high-contrast enhanced image of the nylon thread surface, so that the contrast enhancement is concentrated on the real texture details and the risk of pseudo-texture caused by background gray drift and excessive enhancement is reduced, while the texture direction consistency enhancement, local roughness difference enhancement, and visual readability improvement after the separation of details and background are realized.

The application provides a fluorite ore wall rock alteration detection system based on pattern recognition, and relates to the technical field of geological exploration data processing. Firstly, hyperspectral, RGB texture and laser radar data are spatially corrected and radiometrically calibrated to obtain an aligned spectral cube. After spectral smoothing and multi-order differential enhancement of weak alteration mineral characteristics, a deep unmixing network is used to decompose mixed pixels into each end-member mineral abundance. In the alteration intensity quantification stage, the mineral interaction and synergy characteristics and phase diversity entropy weight are introduced, and a nonlinear coupling fusion model is established to solve the problem of weak alteration zone recognition accuracy decay caused by the linear cumulative model ignoring the paragenetic relationship of minerals. Finally, the structure tensor is used to perform spatial gradient vectorization analysis on the alteration index, and the adaptive threshold segmentation and gradient flow constraint boundary evolution algorithm are combined to output the alteration detection results with intensity grading and accurate boundary positioning, providing pixel-level precision decision support for deep concealed vein exploration.

This invention discloses a method for 3D reconstruction of coaxial digital holographic particle fields combining structural tensors and guided filtering. Specifically, the method involves: normalizing the coaxial digital hologram and performing multi-distance numerical reconstruction to obtain a 3D reconstruction intensity stack; constructing a focus response using structural tensors and generating spatially consistent fusion weights through guided filtering, fusing them to obtain an extended depth-of-field image and a particle soft depth map; constructing a candidate score map based on the extended depth-of-field image to achieve candidate localization, adhesion separation, and diffraction ring suppression; and finely focusing and segmenting the measured particle size within the depth neighborhood, ultimately outputting the particle's 3D coordinates and size parameters. This invention aims to utilize the effective characterization capability of structural tensors for focusing features and the edge-preserving properties of guided filtering to construct a fusion method for multi-depth particle field reconstruction results. This method enhances particle target features while suppressing artifacts and background interference, improving particle localization and measurement accuracy, and achieving higher-precision 3D reconstruction of coaxial digital holographic particle fields.

The present application belongs to the technical field of image recognition, and particularly relates to a plate environmental protection coating surface defect identification method and system based on machine vision, which comprises the following steps: constructing a structure tensor matrix and calculating a local linearity factor; inversely modulating an original gradient vector by using the local linearity factor to construct an inverse modulation flow field; calculating the divergence of the inverse modulation flow field to generate a divergence potential energy diagram, and extracting a suspected defect center according to the local extreme value of the divergence potential energy diagram; and constructing a phase entropy index of the suspected defect center, and comparing the phase entropy index with a bubble determination threshold to determine a bubble defect. The present application can effectively suppress the interference of a high linearity wood grain background, distinguish bubble defects from surface stains and dirt, and improve the accuracy and robustness of plate coating surface defect detection.

The application discloses a kind of tensor field direction constraint weighted fitting fracture prediction method and system.It includes: the original seismic data is carried out dip orientation filter processing, obtains filtered seismic data body;The filtered seismic data body is carried out eigenvalue coherence analysis, obtains coherence volume, and the coherence volume is carried out coherence enhancement processing, obtains enhanced coherence volume;Based on enhanced coherence volume, using tensor direction constraint vector ant colony tracking algorithm, automatically track fracture strike, obtain fracture tracking result;Based on fracture tracking result, construct gradient structure tensor field, use the direction information of gradient structure tensor field to carry out anisotropic Gaussian weighted fitting, obtain the quantitative description result of fracture.The application is through with tensor field direction information as unified constraint throughout fracture prediction whole process, realizes the quantitative description of small scale, low density fracture, and the coincidence rate of prediction result and disclosed fracture area reaches more than 80%, provides reliable basis for mine safety exploitation and reservoir evaluation.

This invention belongs to the field of image processing technology, specifically relating to an online monitoring method and system for the drying morphology of instant goat milk powder. The method includes: acquiring images of the fluidized bed interior to construct a spatiotemporal fluidized image tensor; obtaining fluidization confidence using eigenvalue decomposition of the local spatiotemporal structure tensor matrix to determine the effective fluidized region; performing morphological opening operations on the effective fluidized region using a sequence of structuring elements, statistically summing gray values ​​and constructing a morphological pattern spectrum to obtain the morphological pattern spectrum entropy; obtaining the pulsating velocity vector using an optical flow algorithm, thereby obtaining the average particle pseudo-temperature and coefficient of variation; and obtaining the fluidization steady-state potential energy index based on the morphological pattern spectrum entropy, average particle pseudo-temperature, and coefficient of variation, and generating control commands. This invention achieves monitoring of complex and abnormal conditions such as the initial stage of dead bed formation and channeling through the fluidization steady-state potential energy index.

The present application belongs to the technical field of situation judgment, and particularly relates to a kind of electrode foil corrosion situation judgment method and system, comprising the following steps: S1, the gray scale image of electrode foil to be analyzed is obtained;Based on the composite gradient reflecting the local brightness and texture information of pixel point, the structure tensor is constructed, and the anisotropic diffusion coefficient is determined according to the structure tensor, the gray scale image is processed by iterative anisotropic diffusion filtering using the anisotropic diffusion coefficient, and the filtered image is obtained;S2, the anisotropic diffusion coefficient determined for each pixel point is fused with the pixel intensity of the filtered image to obtain a high-contrast corrosion significance map.The present application can smooth the complex texture and noise in the background area of electrode foil image while maintaining the outline edge information of the corrosion area, solve the contradiction between denoising and edge preservation in traditional filtering method, make the corrosion situation result more reliable, and improve the automation level of electrode foil product quality detection.

This invention relates to the field of watershed topographic monitoring technology, providing a method and system for intelligent watershed monitoring based on UAV oblique photography. The method includes the following steps: Based on a watershed digital elevation model, the flight altitude is dynamically adjusted according to topographic undulations to acquire oblique photographic images; potential feature points are initially screened on each image, and the quality index of each potential feature point is determined based on neighborhood conditions and elevation information; the final retained feature points are determined based on a quadtree algorithm, topographic conditions, and the quality index; the texture information of the surrounding images of the final retained feature points is encoded, and this encoded information is used for comparison and matching in different images; feature point matching is performed, and the matching confidence of each feature point is determined. When the matching confidence is lower than a confidence threshold, dynamic accuracy compensation is performed based on structure tensor and texture gradient to determine the 3D model. This invention can promptly detect and correct errors that occur during the matching process, improving the accuracy and quality of the 3D model.

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.

This invention provides a method and system for displaying veins based on image processing, belonging to the field of image processing. The method includes acquiring a near-infrared vein image to be processed, calculating the structure tensor, selecting the minimum eigenvalue map as a guide image, and calculating the pixel value variance. The original image is decomposed into an initial illumination component and an initial reflection component through guided filtering. The order of a two-dimensional least-squares surface fitting is determined based on the pixel value variance, and the initial illumination component is processed using the fitting to generate a background illumination model. A first vessel response map of the initial reflection component is obtained by calculating the Hessian matrix at multiple scales, and a second vessel response map is obtained by calculating local phase consistency. The two are fused to obtain an enhanced reflection component. A correction matrix is ​​generated by combining the background illumination model and gradient magnitude to correct the enhanced reflection component. The corrected reflection component is multiplied by the background illumination model to reconstruct the vein display image. The display image obtained by this invention is more in line with the needs of practical operation.

The present application belongs to the technical field of image recognition, and particularly relates to a diamond quality detection method and system based on image recognition, comprising the following steps: obtaining a diamond image to be detected, calculating the structure tensor in the local neighborhood of each pixel of the diamond image, constructing an initial structure guide map according to the eigenvalue distribution, and recording the difference between the maximum eigenvalue and the minimum eigenvalue of the structure tensor as an anisotropy degree map; performing multi-scale shear wave transformation on the diamond image, extracting high-frequency subband coefficients at each scale to calculate local energy, and fusing the local energy at each scale to obtain a multi-scale edge saliency map; and fusing the initial structure guide map and the multi-scale edge saliency map to generate a composite guide map. The present application avoids the edge blurring phenomenon caused by traditional filtering, and improves the completeness of diamond surface defect extraction and the accuracy of overall quality detection.

This invention discloses a method and system for image enhancement processing of traumatic neuropathic pain, relating to the fields of data processing and artificial intelligence. Based on multi-scale Gaussian difference edge extraction and structural tensor enhancement, this invention achieves precise preservation of blurred lesion boundaries; independently extracts structural features and optimal scale edge features to solve the problem of unbalanced feature responses caused by different lesion sizes; based on dual-path orthogonal constraints combined with spatial weight modulation, it achieves complete decoupling of lesion features from normal tissue; and utilizes edge features to generate a spatial attention mask to guide the reconstruction process while maintaining the continuity of nerve bundles and avoiding noise amplification. This achieves precise preservation of lesion details, effective suppression of noise, and structural reconstruction of the continuity of nerve bundles, providing clearer and more reliable imaging evidence for clinical diagnosis.

This invention belongs to the field of image analysis technology, specifically relating to an automatic method and system for identifying laser positioning positions in welded pipe cutting. The method includes: acquiring continuous image frames of the welded pipe cutting process; preprocessing the continuous image frames and performing preliminary screening using a first preset brightness threshold to generate a candidate laser point set; extracting gradient features within a preset neighborhood window for each candidate point in the candidate laser point set, constructing a structure tensor and performing eigenvalue decomposition to obtain principal and secondary eigenvalues; and eliminating candidate points based on the principal eigenvalues ​​and a first preset energy threshold, as well as a morphological constraint index calculated from the principal and secondary eigenvalues ​​and a first preset morphological threshold, to obtain finely screened candidate points. This invention can identify the true laser point with the highest signal quality from multiple stable trajectories, achieving laser positioning position identification in welded pipe cutting under strong interference industrial environments.

The application provides a kind of adaptive 3D grid reconstruction and error compensation method for die frame square iron reverse engineering, including using laser scanner to carry out 360 ° scanning on die frame square iron to obtain original point cloud data, and the original point cloud data is registered to the same coordinate system, the original point cloud data is processed by multi-scale bilateral filtering, while constructing point cloud quality evaluation field, identifying data missing area and edge aliasing area, generating point cloud confidence weight map, establishing octree space index and calculating structure tensor to obtain principal curvature value, based on principal curvature and confidence weight, the square iron surface is divided into large plane area, transition feature area and boundary feature area, and the geometric topology skeleton line is extracted.The application decomposes the deviation into systematic error and random error and carries out reverse correction, which not only eliminates the overall deformation error of the workpiece, but also retains the real machining texture, realizes high-quality reverse reconstruction.

The present application relates to the technical field of visual positioning, in particular to a multi-label barcode fast recognition and positioning method and system in a warehouse environment, comprising the following steps: collecting warehouse logistics images to construct a single-channel brightness feature matrix, calculating gradient vectors to construct a structure tensor field and solving texture coherence measure, generating a saliency probability distribution map and using an adaptive threshold to generate a high-frequency texture binary mask, performing a directional morphological closing operation according to the main texture flow direction to generate a closed connected region, extracting geometric moment features to calculate the centroid and output the position index. In the present application, the anisotropic barcode texture features are enhanced by constructing a structure tensor field, and the dynamic morphological closing operation logic based on the main texture flow direction effectively fills the texture gaps while suppressing background discrete noise, solving the positioning failure problem caused by feature aliasing in complex lighting and high-density multi-label scenes.

The application discloses a laminated engraving pattern depth map generation method and system, and belongs to the technical field of image processing, comprising the following steps: gray image acquisition, structure base map extraction, initial depth map generation and laminated engraving depth map output. The application firstly converts a two-dimensional image into a gray image, extracts structure information through structure-texture decoupling processing, and suppresses high-frequency texture interference. By constructing a structure tensor field and correcting gradient amplitude based on consistency measurement, accurate reconstruction of geometric structure is realized. Subsequently, the structure base map gray value is converted into a depth value by using nonlinear mapping, so that the depth map reflects the real geometric relief of the object rather than the surface texture. Finally, the initial depth map is sliced according to the preset laminated engraving parameters, the laminated thickness of each region is dynamically adjusted, a fine and hierarchical engraving depth map is generated, the texture and structure information can be effectively decoupled, the laminated engraving depth map precision is improved, and depth map distortion is avoided.

The application relates to the technical field of machine vision nondestructive testing, and discloses a heavy-load flange fork forge surface crack identification method based on visual detection, which comprises the following steps: collecting point clouds to generate a depth matrix, performing spatial difference extraction of partial derivatives in combination with physical resolution, calculating a principal curvature field to peel off macroscopic deformation, solving a structure tensor and an eigenvalue of the principal curvature field in a discrete window, calculating an anisotropy field and obtaining a structure main direction angle, fusing the eigenvalues to obtain a diffusion kernel physical radius, performing discrete integral accumulation on the anisotropy field along the main direction angle to generate a directional energy field, constructing a threshold surface output binary mask in combination with the extracted energy field statistics, constructing a Hessian matrix by using the second-order partial derivatives of the directional energy field, solving a displacement along the normal to compensate to a grid coordinate and outputting a target coordinate set through mask screening, and effectively peeling off a complex curved surface background and sub-pixel level positioning and identification of surface micro-cracks are realized.

The present application relates to machine vision and image processing technical field, specifically to a kind of workpiece defect feature point's visual image enhancement processing method, including original gradient field generation step: the information of workpiece surface illumination reflection is converted into two-dimensional vector field;Local texture extraction step: introduce structure tensor field and obtain local texture direction field by eigenvalue decomposition;Background flow field construction step: carry out smoothing regularization processing to generate background flow field removed local disturbance;Signal geometry separation step: original gradient field is projected and decomposed into along-texture and inverse-texture gradient component;Gain reconstruction enhancement step: component is handled and reconstructed using differential gain control, and output enhanced image;The present application utilizes the adaptive characteristics of structure tensor field, and realizes the accurate stripping of weak defect signal under strong texture interference.

The application relates to the technical field of industrial component splicing, and discloses an intelligent splicing and positioning control method and system for ceramic and wooden components, which constructs a mapping relationship between a splicing direction coordinate and time by establishing a pixel relationship and a tape running speed, generates a splicing master graph, extracts a splicing seam of the master graph and calculates a structure tensor, obtains a condition number, a main direction angle and a tangent angle, constructs a scalar along the splicing direction and calculates a second-order curvature average, generates a monotone decreasing target weight based on an absolute value of a second-order derivative, performs weighted minimization on a splicing transformation residual error to reevaluate the splicing transformation, extracts a sub-pixel edge along a normal direction of the splicing seam, obtains an actual seam width by combining the pixel relationship, and compensates by using a sensitivity coefficient and a second-order derivative; the difference between the compensated seam width and a target seam width is taken as a control error, proportional integral image servo control is adopted, and a second-order curvature feedforward is introduced, so that high-precision intelligent splicing and positioning of the ceramic and wooden components are realized.

This specification discloses an ultrasound image enhancement method and system. The ultrasound image enhancement method includes: acquiring an ultrasound image; determining the structure tensor of the ultrasound image; determining the diffusion tensor, image mask, and radiation pattern of the ultrasound image based on the structure tensor; performing speckle and noise suppression processing on the ultrasound image based on the diffusion tensor to obtain a first image; performing edge enhancement processing on the ultrasound image based on the image mask and the radiation pattern to obtain a second image; and fusing the first image and the second image to obtain a target image.

The application provides a kind of based on deep learning's radiation pneumonia prediction method and system.The method comprises the following steps: S1, obtains DICOM image, radiotherapy data, clinical data and immunotherapy time series data, and carries out pre-processing;S2, constructs multi-channel three-dimensional structure tensor based on DICOM image;S3, obtains multi-channel input tensor X according to planned CT volume, dose volume and three-dimensional structure tensor;obtain time series characteristic vector and clinical phenotype characteristic vector according to radiotherapy data and clinical data;S4, construct fusion prediction model, and process three-dimensional structure tensor, time series characteristic vector and clinical phenotype characteristic vector, obtain the risk probability of radiation pneumonia;S5, based on the dynamic update of input and re-prediction of updated patient data.The application realizes the deep fusion and dynamic update of multi-modal information, can provide high-precision, risk prediction with spatially interpretable, and provides decision support for clinical intervention.

The present application relates to the field of building structure safety analysis, and particularly relates to a building structure safety analysis method fusing point cloud data. First, based on the obtained three-dimensional point cloud data, a three-dimensional space grid is constructed, and fixed space anchor points are determined. Then, based on the fixed space anchor points, the three-dimensional point cloud data is mapped into the anchor domain, an improved structure tensor neighborhood difference method is introduced, and a structure change intensity index is obtained. Finally, based on the structure change intensity index, an improved time-symmetry second-order difference structure evolution evaluation method is introduced, and a global structure safety index is obtained. The technical problems of being difficult to depict the dynamic evolution process of the structure over time, lacking the ability to identify gradual damage, being sensitive to point cloud data noise and prone to misjudgment, lacking a unified and stable spatial reference system, being difficult to align cross-frame data and affecting the overall analysis accuracy, and relying on experience rules for overall evaluation indexes and lacking data-driven quantitative expression are solved.

The application discloses a high-precision DEM modeling method for a faulted terrain area considering spatial heterogeneity, and in view of the problem that the existing interpolation algorithm has low interpolation accuracy at discontinuous terrain features such as fault lines, a multi-element radial basis function interpolation method considering spatial heterogeneity is provided. Compared with the traditional interpolation algorithm, the application provides a multi-element radial basis kernel function coupling three kinds of terrain feature information of distance, height difference and normal vector, which weakens the influence of sampling points on the opposite side of the interpolation point on the interpolation result, and thus has strong maintaining ability for fold faulted terrains such as ridges and valleys, and jump faulted terrains such as cliffs and terraces. In addition, the application also provides specific experiments, and the experiments show that compared with the interpolation method constrained by the structure tensor and several classical interpolation methods, the average total error of the application method is the smallest, the interpolation performance is the best, and the faulted terrain features can be well maintained, thereby proving the effectiveness of the application method.

This application provides a method, apparatus, and computer equipment for enhancing the visualization of seismic data, relating to the field of seismic data processing technology. The method includes: preprocessing acquired seismic data of the study strata; encoding each pixel in the preprocessed seismic data into a tensor form to obtain a gradient structure tensor; decomposing the gradient structure tensor to obtain eigenvalues ​​and eigenvectors; superimposing votes on the eigenvalues ​​of each pixel within a preset voting domain to form cumulatively voted tensors corresponding one-to-one with each pixel; decomposing the formed cumulatively voted tensors to obtain seismic data enhanced with structural and geological boundary features of the study strata; and visualizing the enhanced seismic data using icons. This method achieves enhanced visualization of the seismic data vector field.