24 results about "Texture orientation" patented technology

The invention relates to the technical field of image processing and cultural relic protection, in particular to a cultural relic image virtual restoration method based on texture reconstruction and color correction, and the method comprises the following steps: S1, obtaining a digital image of the surface of a to-be-restored cultural relic, and carrying out the denoising and brightness equalization processing of the digital image; s2, identifying and segmenting a damaged area in the image; s3, selecting an optimal sample block which is most matched with the structure of the region to be filled; s4, performing adaptive affine transformation on the optimal sample block to generate a reconstructed texture block; s5, carrying out linear transformation to obtain a repaired texture block after color correction; and S6, seamlessly embedding the repaired texture block after color correction into the damaged area. According to the method, the texture direction alignment and the color distribution correction are combined, so that the consistency of the damaged area and the surrounding image in structure and color is realized, and the naturalness and integrity of cultural relic image restoration are remarkably improved.

The invention relates to the technical field of video generation, in particular to a video frame generation system and display equipment based on texture reconstruction and trajectory optimization, and the system comprises a motion breakpoint detection module, a trajectory interpolation frame generation module, a texture anomaly positioning module, a texture interpolation reconstruction module and a video frame synthesis module. According to the method, an edge feature map is generated through edge detection and non-maximum suppression, the boundary recognition precision is improved, noise is suppressed, candidate regions are divided through region growth, the local feature extraction efficiency is enhanced, global redundancy is reduced, three-frame optical flow analysis captures trajectory breakpoints, and motion vector correction path deviation is predicted in combination with a Lucas-Kanade method. Color consistency verification optimizes interpolation to reduce cross-frame abrupt change; 64 * 64 grids and a gray level co-occurrence matrix locate texture abnormity; five-frame similarity strengthens time sequence stability; Gabor filtering extracts a main direction and combines an adjacent frame reconstruction texture direction to ensure consistency; and trajectory smoothness and visual consistency are optimized through multi-frame cooperation and layered detection.

The invention provides a low-light environment imaging enhancement method based on multi-frame synthesis, and the method comprises the steps: carrying out the independent geometric correction of each region according to the optimal registration transformation matrix of each region, and obtaining the image data of each region after geometric correction; weight distribution is carried out on the high-frequency texture components according to the texture consistency evaluation result, if the texture direction deviation angle is smaller than a preset angle threshold value, the weight coefficient of the frame is improved, the high-frequency components of the multiple frames are fused, and a high-frequency synthesis component with enhanced texture details is obtained; reconstructing the texture detail enhanced high-frequency synthetic component and the contour retentivity optimized low-frequency component, and performing multi-scale fusion processing on the reconstructed image to obtain a final nighttime animal observation synthetic image; in an image quality evaluation module of the camera APP, animal key feature points are extracted from the synthesized image, the reliability of animal feature recognition is verified by calculating the stability of feature point descriptors, and a verification result of the reliability of animal feature recognition is obtained.

The invention discloses a method and a system for judging an end point of a mutton unfreezing state based on machine vision, particularly relates to the technical field of image recognition, and is used for solving the problems of image feature distortion and poor judgment accuracy caused by optical interference caused by a meat surface ice-water mixed phase state in an unfreezing process in an existing method. An unfreezing process image is obtained and converted into a color space containing a brightness component, a specular reflection area and a non-specular reflection area are segmented based on the brightness component, and the specular reflection area image is converted into a topological network diagram to calculate connectivity evolution parameters. And performing texture direction field analysis on the non-specular reflection region to calculate texture direction consistency recovery degree features, further performing collaborative mode analysis on a time sequence of the two types of features to obtain a feature vector, and finally inputting the feature vector into a pre-trained classification model to judge an unfreezing stage. And judging that an unfreezing end point is reached when the stage accords with a preset end point.

The invention provides a metal slitting machine cutter group backlash automatic adjustment method, which comprises the following steps of: acquiring a metal coiled material surface image through optical scanning equipment, performing high-resolution imaging processing aiming at texture direction distribution, extracting surface texture direction distribution characteristics, and combining preset cutting direction setting to obtain initial included angle data; according to the preliminary included angle data, analyzing and judging the correlation between the texture strength difference and the relative angle deviation, and determining a preliminary backlash adjustment amplitude; aiming at the initial backlash adjustment amplitude, combining material anisotropy and material thickness influence, and integrating cutting stress influence and parallel and vertical scene resistance to obtain adjusted backlash dynamic compensation parameters; and if the edge defect associated area does not accord with the defect rate monitoring index, key points are extracted from stress distribution, cutting tool wear data are combined, backlash dynamic compensation parameters are adjusted, and an optimized backlash configuration value is obtained.

This invention discloses a surface-coated cutting tool with a preferred texture orientation, its preparation method, and its application. The surface-coated cutting tool includes a tool substrate and a surface coating disposed on the tool substrate. At least one layer of the surface coating is an α-Al₂O₃ coating. The α-Al₂O₃ coating has a preferred texture orientation relative to the {1010} and {0012} crystal planes, and the texture coefficient satisfies the following characteristics: 5.0 ≤ TC(1010) + TC(0012) < 9.0, and 3.0 ≤ TC(1010) < 9.0, 0 < TC(0012) < 4.0. The preparation method includes depositing the coatings on the tool substrate using a CVD process. The surface-coated cutting tool of this invention possesses both excellent wear resistance and anti-chipping properties, exhibiting superior performance in the machining of materials such as cast iron, stainless steel, and alloy steel.

The invention discloses a stem cell-oriented culture environment real-time monitoring method and system, and particularly relates to the field of stem cell-oriented culture environment real-time monitoring. Comprising the following steps: performing sub-pixel-level interpolation reconstruction, local contrast enhancement processing, multi-scale wavelet transform and local gradient direction analysis on acquired original cell image data, extracting internal texture feature information of cells, and constructing a texture orientation distribution tensor data set; and performing principal component feature extraction, kernel principal component analysis and manifold embedding dimensionality reduction on the texture orientation distribution tensor data set, constructing a low-dimensional feature space maintaining a local structure, and generating a cell texture density distribution model based on kernel density estimation. The early recognition of the cell fate heterogeneity and the dynamic intervention of the culture process under the unmarked condition are realized through time sequence modeling and classification discrimination based on cell image texture evolution characteristics in combination with density analysis and environmental feedback regulation.

The application discloses an industrial product micro defect visual detection system and method, belongs to the product detection technical field, obtains the multi-source image data of the target industrial product under multiple detection angles and illumination conditions, and establishes an image information matrix; the image is subjected to region segmentation and texture enhancement, and a local texture direction inconsistency degree parameter is extracted; the pixel ratio is normalized and analyzed under different spectral channels, and a multispectral reflectance anomaly index is calculated; a deep convolution recognition model is constructed; the model is used for reasoning on the image, and a defect judgment result and a confidence score are output; whether it is a defect area is determined based on a dynamic threshold mechanism, and a detection report containing defect position information and a visualized heat map is output; the application realizes multi-dimensional fusion recognition of texture structure disturbance and spectral response anomaly, effectively improves micro defect recognition precision, has high robustness, automation and engineering practicability, and is suitable for high-precision quality control requirements in various industrial scenes.

The application provides a method and device for determining an intra prediction mode and electronic equipment, and relates to the technical field of video coding, wherein the method comprises: dividing a minimum coding unit (LCU) in a target coding unit (CU) of a target image into a plurality of sub-block units; determining texture complexity of the target CU in a plurality of directions according to texture complexity of each of the sub-block units in the plurality of directions; and determining an intra prediction mode of the target image according to a direction corresponding to a maximum value of the texture complexity of the target CU in the plurality of directions, in a case where a difference between the maximum value and a minimum value of the texture complexity of the target CU in the plurality of directions satisfies a preset condition. According to the embodiment of the application, several prediction modes most likely to be selected are screened according to the texture direction of the CU to serve as candidate modes, the number of candidate prediction modes in the rough selection of the intra prediction mode is reduced, and the encoding time is saved.

The invention discloses a general T-Student kernel function-based bamboo strip broken simple data registration splicing method, which comprises the following steps of: taking to-be-spliced bamboo strip broken simple data as input of a point cloud splicing model of the bamboo strip broken simple to obtain a splicing result of the spliced bamboo strip broken simple; the model comprises a geometric feature extraction module, a texture feature extraction module and a joint optimization registration module. The geometric feature extraction module constructs a multi-scale geometric descriptor fused with region connected graph convolution by calculating a local normal vector, and generates a geometric label; a texture feature extraction module extracts a texture gradient vector and generates a binary direction mask; and the joint optimization registration module integrates the outputs of the first two modules, constructs a multi-modal error function fusing geometric-texture errors, performs weighted optimization by adopting a generalized T-Student kernel function, and finally outputs a splicing result through iterative calculation. According to the method, multi-scale geometric features, texture direction constraints and robust kernel function optimization are combined, and high-precision automatic splicing of the bamboo strips is achieved.

This invention relates to a texture primitive extraction method for tire X-ray images, addressing the challenge of accurately obtaining pixel-by-pixel texture information at the individual cord level while suppressing background interference. It falls under the field of computer vision and image processing technology. The method combines frequency domain analysis to obtain texture direction and spacing, utilizes this information to construct a mesh mask, and further combines background point extraction with real background reconstruction to filter out specific background regions in the original image. Under directional constraints, the remaining texture mesh is continuously tracked to obtain pixel-by-pixel texture information, thus achieving texture primitive extraction. This provides a more reliable foundation for subsequent pathological detection, structural analysis, and cord-level texture modeling.

The invention provides a wood board surface defect detection method and system based on machine vision and a medium, and the method comprises the steps: controlling the angle of a light source array based on a light source control instruction, and obtaining board images of multiple angles; carrying out fusion processing on the plate images of the multiple angles based on a maximum value fusion method, calculating an image gradient, generating a gradient tensor of the image, and carrying out eigenvalue decomposition on the gradient tensor to obtain texture direction distribution information; performing region segmentation on the enhanced image, constructing a texture prediction model, and generating a model prediction result of each sub-region based on the texture prediction model; analyzing a model prediction difference value, and generating a residual response diagram; analyzing the residual response value of each sub-region to obtain surface defect information of the plate; according to the method, the plate images at multiple angles are collected, fusion processing and eigenvalue decomposition are carried out, so that texture direction distribution is accurately analyzed, defects on the surface of the plate are accurately analyzed according to the residual response diagram, and the detection precision is improved.

The invention provides a multi-stage image enhancement and adaptive MPC fused unmanned ship control method, and relates to the technical field of unmanned ship intelligent control. The method comprises the following steps: acquiring a tunnel inner wall image acquired by an unmanned ship, analyzing a leading texture direction formed by long-term water flow scouring, and determining a texture direction reference for constraining image enhancement; performing multi-stage image enhancement based on the texture direction reference to generate an enhanced image for suppressing reflection interference; a texture abnormal area of the enhanced image is predicted according to the texture direction reference, and an inspection target area is determined; in combination with the current navigation state of the unmanned ship and the position of the inspection target area, a navigation trajectory is predicted by adopting a self-adaptive model prediction control method, and a course angle reaching the inspection target area is determined and output accordingly, so that accurate inspection and stable navigation control of the unmanned ship in the complex tunnel environment are realized.

The application relates to the technical field of image recognition, and discloses a metal plate defect point detection method and system. The method comprises the following steps: obtaining a surface image of a target plate as an original image of the target plate; performing noise suppression on the original image to obtain an enhanced image of the target plate; performing texture direction feature extraction on the enhanced image to obtain a texture trend distribution map of the target plate; performing differential analysis on the texture trend distribution map and a preset standard texture direction map to obtain direction abnormal points of the enhanced image; performing gray scale statistics on the direction abnormal points to obtain a local gray mean value of the target plate, and determining a pixel point with a gray value lower than the local gray mean value as a suspected defect point; and performing morphological clustering on the suspected defect point to obtain a defect area of the target plate. The application can improve the defect point detection efficiency of the metal plate.

The invention discloses a blanking finished product quality detection method based on texture direction analysis, and belongs to the technical field of product quality detection, and the method comprises the steps: recognizing the periodic change of scattering angle distribution through spectrum analysis, analyzing the incidence relation between a surface roughness Ra value and a texture main direction deviation angle, and determining the quality of a blanking finished product. According to the method, statistical distribution of a texture main direction deviation angle is evaluated to obtain a dispersion degree of a texture direction, clustering grouping is carried out on the dispersion degree, a texture confusion subset and a texture consistent subset are divided, and an abnormal concentrated area of scattering angle distribution is extracted from the texture confusion subset. And dynamically adjusting the quality judgment benchmark according to the dispersion degree of the texture direction so as to identify the hidden defect caused by the unstable process.

The invention provides a vacuum demolding efficiency evaluation method based on an intelligent algorithm, and the method comprises the steps: comprehensively detecting the surface of a plastic substrate, obtaining a surface roughness parameter and texture directivity feature data, determining the specific mode of microscopic peak valley distribution and texture density distribution, and obtaining a texture feature distribution diagram of the surface of the substrate; aiming at the bonding strength space distribution parameters, judging whether a non-uniform bonding strength region caused by too high local defect proportion exists or not; if a non-uniform bonding strength area is detected, determining an optimal combination of a stripping speed influence parameter and a stripping force fluctuation value in a stripping process in combination with surface energy difference and texture density distribution data; and analyzing the influence of the residual bonding ratio and the fragmentation accumulation effect on the demolding quality, and judging whether the fragmentation degree exceeds an acceptable range or not.

This invention relates to a method for improving the texture orientation and processing properties of Fe-6.5%Si steel, belonging to the field of material modification technology. It includes composition adjustment, determining the normalization temperature range and cooling medium, cold rolling and annealing treatments, calculating the lattice slip energy of the cold-rolled non-oriented steel sheet, electrical pulse modification, and temperature control. This invention alters the magnetic and deformation properties of Fe-6.5%Si alloys through the addition of alloying elements and pulsed current treatment, resulting in low-iron-loss, low-noise, high-silicon electrical steel for power transformers that can be produced using rolling processes. The silicon steel sheets prepared using this invention have been validated in engineering applications. Third-party testing has shown that their performance indicators meet the requirements for silicon steel sheets used in transformers, demonstrating strong engineering practicality and enabling actual industrial production.

The invention relates to the field of image processing, in particular to a surface defect detection method and system for automobile parts, and the method comprises the steps: obtaining a preprocessed surface gray-scale map of the automobile parts; calculating the comprehensive similarity weight of the reference point and the target point based on the obtained texture direction difference, the obtained direction consistency difference and the obtained gray difference, calculating the filtering gray value of the target point based on the comprehensive similarity weight and the gray value of the target point, and traversing to obtain the filtering gray value of each pixel point of the surface gray-scale map; obtaining a texture suppression image, and training a preset defect detection model based on the texture suppression image to obtain a trained preset defect detection model; and inputting a to-be-detected texture suppression image into the trained preset defect detection model, and outputting a defect type. Through the technical scheme of the invention, the precision of a defect detection result can be improved.

This invention discloses a method for obtaining high-strength, high-conductivity copper alloys through gradient twinning, belonging to the field of high-strength, high-conductivity electronic device fabrication technology. The fabrication method includes: obtaining plates with various texture orientations through directional solidification technology, stacking them sequentially from hard orientation to soft orientation to prepare multilayer plates with different texture orientations, and then performing cumulative roll bonding on the multilayer plates. Through this method, the gradient twinned copper alloy prepared by this invention can effectively improve the conductivity of the alloy while increasing its strength. This design and fabrication method for high-strength, high-conductivity copper alloys has significant guiding significance for adapting to the development of industries such as electronics and information, future transportation, and aerospace, and has very good industrial application prospects.

The application discloses a zirconium alloy strip and a texture control method and application thereof. The zirconium alloy strip is obtained by adopting (alpha+beta) two-phase zone temperature (700 DEG C-850 DEG C) hot rolling or direct hot rolling under alpha phase zone hot rolling conditions, adopting cross rolling under the condition of one-time hot rolling under the alpha phase zone hot rolling conditions, and performing quenching treatment after one-time hot rolling under the alpha phase zone hot rolling conditions, so that the texture of the zirconium alloy strip is effectively controlled, the texture orientation factor is equivalent to that of imported Zr-4, and the performance requirement of the zirconium alloy strip is met.

The invention discloses a solid wood furniture board intelligent nesting method considering texture direction constraint, which comprises the following steps of: synchronously generating a pixel-level defect segmentation image and a texture direction field from a board digital image by adopting a deep neural network; for each to-be-trepanned part, constructing a comprehensive value map by fusing a defect repulsive force field generated by the defect segmentation map and a texture coordination map generated by the texture direction field; iteratively searching the optimal placement pose of each part on the value map by adopting a hybrid optimization strategy combining an artificial potential field method and simulated annealing; and after the pose of one part is determined, the area occupied by the part is updated to be an unavailable area, and the process is repeated for the next part until nesting of all the parts is completed. The problems that in the prior art, defect perception and trepanning optimization are disjointed, texture constraint processing is insufficient, and an optimization algorithm is prone to falling into local optimum are solved, and the plate utilization rate and the product aesthetic quality can be improved.

The invention discloses a structure surface damage image enhancement method and system based on image processing, and relates to the technical field of image processing enhancement. Main texture direction information of each pixel in an image is accurately identified through a local structure tensor extraction module, and a directional response mechanism is introduced into a direction sensitive suppression and orthogonal enhancement module; and the enhancement process has spatial selectivity. Compared with a traditional uniform enhancement mode, the system can automatically press redundant high-frequency components in the background texture main axis direction, meanwhile, a potential damage area orthogonal to the main texture is amplified, and a target structure can still be effectively separated in the background with strong texture interference. The tail end of the system reconstructs an enhanced image through a multi-scale convolution kernel, and structure disturbance responses under different scales are fused, so that the method is not only suitable for fine depiction of micro cracks, but also can consider morphological expression of corrosion spots or scratch channels in a larger range.

The invention relates to the technical field of machine vision and image processing, in particular to a visual image enhancement processing method for workpiece defect feature points, which comprises the following steps: an original gradient field generation step: converting workpiece surface illumination reflection information into a two-dimensional vector field; a local texture extraction step: introducing a structure tensor field and obtaining a local texture direction field through characteristic decomposition; a background flow field construction step: executing smooth regularization processing to generate a background flow field with local disturbance removed; a signal geometric separation step: decomposing the original gradient field projection into a forward texture gradient component and an inverse texture gradient component; a gain reconstruction enhancement step: processing and reconstructing the components by using differential gain control, and outputting an enhanced image; according to the method, the adaptive characteristic of the structure tensor field is utilized, and accurate stripping of weak defect signals is realized under strong texture interference.