58 results about "Texture model" patented technology

The invention relates to the field of commodity visualization, in particular to a commodity display interaction visualization method and device. The method comprises the following steps: collecting a multi-azimuth image of a commodity, carrying out three-dimensional texture modeling, and constructing a three-dimensional texture mapping model; performing material light rendering on the three-dimensional texture mapping model to generate a material rendering result; collecting an environment detection image of a commodity display environment, and performing environment illumination adaptation compensation on a material rendering result to obtain an illumination compensation rendering commodity; carrying out attribute information visual layout on the illumination compensation rendering commodity to obtain a commodity visual space; and carrying out interaction response animation analysis according to the commodity visualization space, carrying out multi-target parallel rendering, and executing commodity interaction visualization operation. The form and surface details of the commodity in the real world are accurately restored, the visual reality sense is improved, and the interactive experience feeling of browsing the commodity by a user is enhanced.

The invention provides a hull surface defect detection system based on machine vision, and relates to the technical field of data processing. The image correction module is used for carrying out illumination equalization processing and geometric distortion correction; the region construction module is used for identifying a defect-free stable region and generating reference region data which comprises a brightness model and a texture model; the candidate generation module is used for detecting a region where texture interruption or abnormal bright spots exist locally to form candidate defect data, and the candidate defect data comprise pixel positions and local contrast parameters; the stability judgment module is used for carrying out projection matching in the multiple frames of images and simultaneously carrying out joint comparison with the brightness model and the texture model of the reference area data to form real defect data and false defect data; the result output module is used for generating a detection result containing defect coordinates, defect contours, image frame numbers and interference sample prompts; the accuracy of hull surface defect detection is improved.

The invention relates to the technical field of three-dimensional scanning, and discloses a binocular three-dimensional scanning system and method with a texture mapping function. The system comprises a depth data acquisition module, a depth data correction module, a point cloud topology construction module, a texture mapping engine module and a mapping optimization module. The depth data acquisition module synchronously acquires a left view and a right view of a target object through a binocular camera array, and generates an original depth image data set; the depth data correction module performs abnormal value detection on the original depth data, extracts discrete distribution through aggregation boundary analysis and replaces abnormal values; the point cloud topology construction module generates a three-dimensional point cloud spatial topology structure, and associates point cloud coordinates with left view pixel positions; the texture mapping engine module projects left view RGB data to the point cloud to generate an initial texture map; and the mapping optimization module verifies the local color consistency through the generative adversarial network, and outputs an optimized three-dimensional texture model. The system improves the texture precision and authenticity of the three-dimensional model.

The invention discloses a metal product defect detection method and system based on image recognition. The method comprises the following steps: firstly, executing reflection disturbance digestion processing on a surface image of a to-be-detected metal product to obtain a reflection digestion image; obtaining surface reference texture features of the defect-free metal product, and generating a reference texture model on the basis of a texture distribution rule, a gray average value and texture continuity; performing defect texture gradient separation on the reflection resolution image based on a reference texture model, positioning an abnormal region, and performing segmentation to obtain a suspected defect texture region; performing boundary pixel reconstruction on the suspected defect texture region to obtain a defect texture reconstruction region; obtaining a target defect texture region through local variance enhancement and neighborhood correlation analysis; and finally, the overexposure area is positioned, gray inverse stretching processing is performed, abnormal pixel group feature clustering is performed on the preprocessed defect area, and then a surface defect detection result is output, so that the precision and accuracy of metal product surface defect detection are improved, and the false detection rate is reduced.

The invention discloses a multi-modal lightweight forest fire detection method suitable for a vertical take-off and landing fixed-wing unmanned aerial vehicle platform, and the method comprises the steps: carrying out the feature alignment and weight fusion of a visible light image and an infrared image, which are synchronously collected by an unmanned aerial vehicle, through a multi-modal early fusion method, and obtaining a fusion feature; carrying out lightweight down-sampling and multi-scale feature extraction on the fusion features to obtain a multi-scale feature set; performing cross-scale feature fusion on the multi-scale feature set by adopting a bidirectional feature pyramid network, and extracting dynamic change information to obtain time sequence features; performing multi-scale context enhancement on the time sequence features by using a spatial pyramid method based on cavity convolution, and performing global context and local texture modeling by using a lightweight Transform module to obtain high-level semantic features; and based on the high-level semantic features, a target detection frame, fire level classification, prediction uncertainty estimation and a pixel-level fire probability graph are generated in parallel through a multi-task output header.

The invention discloses a medical image segmentation method based on boundary constraint. The method comprises the following steps: S1, obtaining a medical image amplitude spectrum and a medical image phase spectrum; s2, forming a structure sensing frequency domain disturbance result; s3, obtaining a frequency domain enhanced medical image; s4, calculating structure perception medical image boundary enhanced attention output features; s5, performing global relation modeling and local texture modeling on the frequency domain enhanced medical image sequence; s6, generating an edge heat map; and S7, performing bidirectional gating interaction on the edge heat map and the output of the linear self-attention encoder by adopting a double-domain interaction fusion strategy to generate a double-domain interaction fusion feature map, performing up-sampling and convolution processing on the double-domain interaction fusion feature map in a decoder, and outputting a boundary enhancement segmentation feature map. According to the invention, the structure perception capability of the model in a medical image scene with fuzzy anatomical structure boundary and low texture contrast is effectively improved.

The invention discloses a hierarchical image rain removal method based on enhanced rain stripe perception. According to the method, for the problem of image quality degradation in a rainy day environment, an enhanced rain stripe perception feature enhancement module E-RAFEM is designed, two core components of a learnable direction filter and rain stripe texture modeling are integrated, and the directivity and linear texture features of rain stripes are accurately modeled. An encoder-decoder backbone network based on Transform is adopted, and E-RAFEM modules are embedded in the first three encoding levels, so that a hierarchical rain stripe processing mechanism is formed. A progressive multi-scale fusion mechanism is introduced, multi-scale features are captured through convolution kernels with different expansion rates, and gradual integration is carried out in a progressive mode to avoid information loss. According to the enhanced rain stripe sensing network ERA-Net provided by the invention, high-quality recovery of images under various complex rainy day conditions is realized through accurate rain stripe feature modeling and hierarchical processing strategies.

The invention discloses a Hololens-oriented single-view texture-free object pose estimation method, belongs to the technical field of augmented reality, and solves the problem that an existing Hololens pose estimation method cannot perform accurate pose estimation on a texture-free model through single-view data under the conditions of no marker and no pre-training. According to the method, multi-modal data acquisition, self-supervised segmentation, self-adaptive point cloud denoising and geometrically-driven pose optimization are integrated, and accurate pose estimation is carried out on a texture-free object only through single-view-angle data under the condition that no marker exists and pre-training is not carried out. Through multi-modal data and spatial transformation, a point cloud reconstructed by a Depth image is mapped to a visual angle of an RGB camera, so that the point cloud is conveniently filtered by using a target mask obtained on the RGB image. Meanwhile, rendering templates under multiple visual angles are generated by rendering the CAD model, obstacles caused by no texture of the target model are avoided, meanwhile, the CAD model does not depend on pre-training, and the zero sample learning ability is provided.

The invention discloses a welding quality online detection and optimization method and system, and the method comprises the steps: carrying out laser scanning, and obtaining inertial navigation data of a mobile robot; performing motion compensation and multi-frame alignment on the original point cloud data through the inertial navigation data; starting an image acquisition device to carry out image acquisition; performing spatial alignment on the visible light image and the alignment point cloud data through inertial navigation data; visible light texture data obtained by aligning the image sequence is mapped to the 3D point cloud model, and the welding quality is detected through the 3D texture model; and according to the welding quality detection result, welding parameters are adjusted in a self-adaptive mode. The problems of image blurring and dislocation caused by track deviation or environment vibration in long-distance movement detection in a non-special site are effectively solved, the geometric accuracy and stability of point cloud data are remarkably improved, and a reliable foundation is laid for subsequent detection.

The invention discloses a short wave infrared small sample three-dimensional Gaussian three-dimensional reconstruction method based on knowledge assistance. The method comprises the following steps: firstly, converting a three-dimensional structure model into a point cloud, carrying out structure perception point cloud fusion on the point cloud and an original target point cloud, and then carrying out initial training to realize geometric construction based on structure prior; then generating a short-wave infrared simulation image based on the three-dimensional model, and performing target instance driven cross-domain adaptation training on a pre-trained diffusion model to obtain a cross-domain diffusion model; and finally, in combination with a cross-domain diffusion model and a structure prior driven geometric construction result, triple constraints are added to reconstruct a three-dimensional image. Through staged modeling under structure guidance, the fuzzy or artifact risk caused by directly introducing misaligned or error accumulated geometric information into the texture stage is avoided, and a stable structure guidance image with clear components is provided for the texture modeling stage; and the generation stability and the structural fidelity of the diffusion model under complex observation conditions are effectively enhanced.

The application relates to the technical field of image processing, in particular to a thermal imaging visualization method and system for energy efficiency analysis of a roasting process. The method comprises the following steps: acquiring a thermal imaging image of a roasting kiln body surface, and calculating local complexity indexes and local anisotropy indexes of each pixel point in the thermal imaging image; extracting a multi-scale and multi-direction texture feature set of each pixel point in the thermal imaging image; calculating scale weights of each scale and direction weights of each direction, and performing weighted fusion on the texture feature set to obtain a fusion feature vector of each pixel point; establishing a normal texture model based on the fusion feature vector, and calculating an abnormal score image of the thermal imaging image to determine an abnormal heat loss area and perform visualization. Through the technical scheme, different scale texture changes can be effectively identified, and the detection accuracy of the abnormal heat loss area is improved.

This invention provides a weight training method and system for generating geological lithological texture models based on LoCon, belonging to the field of geological lithological texture generation technology. It employs an LDM model as the large model framework for weight training and texture generation. Convolutional layers are integrated into the convolutional weights of the residual blocks in the U-Net model within the LoCon model. The low-rank matrix of the LoCon model is added to the pre-trained weight matrix in the U-Net model to construct the generated geological lithological texture model. The model is trained using a training set. The trained weights are output, loaded into the large model framework for inference, and the training parameters are adjusted based on the inference results. Training is repeated until the preset inference result requirements are met. An API is provided to create an interface between the LDM large model framework and the trained weight matrix. This fills a gap in the geological industry's material library and lowers the technical threshold for geologists to participate in digitization.

The invention relates to the technical field of three-dimensional visualization, in particular to a three-dimensional visualization collaborative design method under a complex terrain, and solves the technical problem of poor three-dimensional modeling effect in the prior art. The method comprises the following steps: acquiring point cloud data of a target object scanned by a plurality of scanning sites, and acquiring a multi-view image of the target object acquired by image acquisition equipment; the multi-view image comprises a plurality of target images collected by the target object in different view directions; identifying an abnormal brightness area from the multi-view image, and repairing the abnormal brightness area to generate a repaired multi-view image; and performing three-dimensional reconstruction and image texture mapping based on the repaired multi-view image and the point cloud data to generate a three-dimensional scene texture model.

The invention relates to the technical field of image processing, in particular to a textile material surface defect detection system based on machine vision, the system comprises a processor and a memory, the processor executes a computer program of the memory to realize the following steps: obtaining a spectrogram of a textile material to be detected and the amplitude of each frequency component in the spectrogram, dividing the spectrogram into at least two initial sub-regions, acquiring a peak value prominence degree, an amplitude distribution obvious degree and a dominant frequency existence index of any initial sub-region, and according to the peak value prominence degree, the amplitude distribution obvious degree and the dominant frequency existence index of any initial sub-region, determining the dominant frequency existence index of any initial sub-region; adjusting any initial sub-region to obtain a target sub-region; and obtaining the target sub-region corresponding to each initial sub-region, obtaining the dominant frequency in each target sub-region, constructing the normal texture model, and carrying out surface defect detection on the to-be-woven material, so that the detection result of the surface defect of the textile material is improved.

The invention relates to the technical field of man-machine interaction, and discloses a multi-modal AI and emotional intention recognition three-dimensional content real-time generation method, which integrates a multi-modal AI collaborative generation module, a dynamic emotional intention recognition module, a three-dimensional content real-time generation module and a holographic software and hardware collaborative module, the method comprises the following steps: realizing an immersive holographic interaction and multi-modal AI collaborative generation module, constructing a unified semantic space by adopting CLIP + VATT, analyzing an instruction by adopting LLM to generate parameters, generating 2D content by adopting Diffusion, converting NeRF into a textured 3D model in real time, and realizing sketch / semantic driven parameterization generation by combining ControlNet and LoRA. According to the scheme of the invention, the breakthrough efficiency can be improved, and the AI driven automatic generation technology is realized; a traditional 3D content production process which needs to be completed in several days can be compressed to be completed in several minutes by means of collaborative optimization of models such as Point-E, NeRF and Diffusion and combining ControlNet structured control and LoRA low-rank fine tuning, and the modeling efficiency is integrally improved.

Embodiments of this application provide a method for generating a three-dimensional texture model of a city based on composite data, and a device. The method includes: obtaining satellite remote sensing data of a target region, where the satellite remote sensing data is used to determine a three-dimensional framework model and spatial coordinates of a to-be-measured object; obtaining video data within a preset range of the spatial coordinates of the to-be-measured object, where the video data is used to determine a surface texture of the to-be-measured object; and generating a three-dimensional texture model of the to-be-measured object based on the surface texture and the three-dimensional framework model of the to-be-measured object, where the three-dimensional texture model of the to-be-measured object is used to generate a three-dimensional texture model of the target region. The satellite remote sensing data and the video data within the preset range of the spatial coordinates of the to-be-measured object are obtained at low costs, so that costs and difficulty of constructing the three-dimensional texture model of the city are reduced, and costs and difficulty of updating the three-dimensional texture model of the city are reduced.

This application discloses a method and system for detecting defects in metal products based on image recognition. The method first performs reflection perturbation elimination processing on the surface image of the metal product to be detected to obtain a reflection elimination image; then, it acquires the surface reference texture features of the defect-free metal product, and generates a reference texture model based on texture distribution patterns, gray-level mean, and texture continuity; next, it performs defect texture gradient separation on the reflection elimination image based on the reference texture model, locates abnormal regions, and segments them to obtain suspected defect texture regions; then, it performs boundary pixel reconstruction on the suspected defect texture regions to obtain defect texture reconstruction regions; the target defect texture region is obtained through local variance enhancement and neighborhood correlation analysis; finally, it locates overexposed regions and performs gray-level inverse stretching processing, and outputs the surface defect detection results after clustering abnormal pixel group features of the preprocessed defect regions, thereby improving the accuracy and precision of surface defect detection of metal products and reducing the false detection rate.

The invention discloses a palm vein recognition method and system based on multispectral imaging and deep learning and a storage medium, and relates to the field of biological feature recognition, and the method comprises the steps: obtaining the multi-dimensional information of a palm through the synchronous collection of near-infrared, short-wave infrared and visible light wave band images, and then carrying out the adaptive weighted fusion and enhancement processing of a multispectral image, thereby achieving the recognition of the palm vein. In the core recognition step, a double-branch deep learning model is adopted, one branch extracts static structure features from the vein feature map, the other branch analyzes blood flow dynamic features from a time sequence image, and finally the static structure features and the blood flow dynamic features are fused for integrated living body detection and identity recognition. Through three-dimensional vein texture modeling and a dynamic living body detection anti-counterfeiting mechanism, faking attacks of photos, silica gel molds and the like are completely eradicated, and the problems that in a complex environment, performance of a traditional method is reduced, faking attacks are likely to happen, and speed and precision are difficult to consider at the same time are effectively solved.

The application discloses a hierarchical image rain removal method based on enhanced rain streak perception. In view of the image quality degradation problem in rainy environment, an enhanced rain streak perception feature enhancement module E-RAFEM is designed, which integrates a learnable direction filter and a rain streak texture modeling two core components, and accurately models the directionality and linear texture features of rain streaks. A Transformer-based encoder-decoder backbone network is used, and the E-RAFEM module is embedded in the first three encoding levels to form a hierarchical rain streak processing mechanism. An incremental multi-scale fusion mechanism is introduced, multi-scale features are captured through different expansion rate convolution kernels, and an incremental method is used to gradually integrate to avoid information loss. The enhanced rain streak perception network ERA-Net proposed by the application realizes high-quality recovery of images under various complex rainy conditions through accurate rain streak feature modeling and hierarchical processing strategy.

To provide a lump palpation model which allows a user to experience the simulated sensation of breast cancer lumps by gently pressing and stroking the model with fingers, and which can be handily used while taking a bath, etc.SOLUTION: A lump palpation model 1 for allowing a user to experience the simulated feel of lumps present inside a body through tactile sensation is provided, the lump palpation model comprising a main body 2 having a mounting surface 21 for mounting the model and a touching surface 20 to be touched for a simulated experience, and simulated lumps 3 simulating lumps embedded in the main body 2, and being configured to float on water. The mounting surface 21 has an adhesive surface that can be adhered to a wall or the like.SELECTED DRAWING: Figure 2

The application provides a hull surface defect detection system based on machine vision, and relates to the technical field of data processing.The system comprises an image acquisition module, an image correction module for performing illumination balancing and geometric distortion correction, a region construction module for identifying a stable region without defects, generating reference region data, including a brightness model and a texture model, a candidate generation module for detecting a region with local texture interruption or abnormal bright spots, forming candidate defect data, including pixel position and local contrast parameters, a stability determination module for performing projection matching in multiple images and simultaneously performing joint comparison with the brightness model and the texture model of the reference region data, forming real defect data and false defect data, and a result output module for generating a detection result containing defect coordinates, defect contours, image frame numbers and interference sample prompts.The application improves the accuracy of hull surface defect detection.

The learning data generation apparatus (100) includes: a 3D model acquisition unit (110) that acquires 3D model information representing a 3D model of an object; a local image acquisition unit (120) that acquires local image information representing an image region of an object captured in a captured image, i.e., a local image; a texture coordinate acquisition unit (130) that acquires 2D texture coordinates for texture mapping of the local image to the 3D model based on the local image information and the 3D model information; a rendering condition acquisition unit (140) that acquires rendering condition information representing rendering conditions, which are conditions for rendering a textured 3D model for which texture mapping of the local image to the 3D model has been performed based on the 2D texture coordinates; a 2D image acquisition unit (150) that acquires 2D image information representing a 2D image by rendering the textured 3D model based on the rendering condition information; and a learning data output unit (190) that outputs 2D image information.

The invention relates to the technical field of image processing, in particular to a thermal imaging visualization method and system for roasting process energy efficiency analysis, and the method comprises the steps: obtaining a thermal image of the surface of a roasting kiln body, and calculating a local complexity index and a local anisotropy index of each pixel point in the thermal image; extracting a multi-scale and multi-direction texture feature set of each pixel point in the thermal image; calculating a scale weight of each scale and a direction weight of each direction, and performing weighted fusion on the texture feature set to obtain a fusion feature vector of each pixel point; and establishing a normal texture model based on the fused feature vector, and calculating an abnormal score graph of the thermal image to determine an abnormal heat loss area and perform visualization. According to the technical scheme, texture changes of different scales can be effectively identified, and the detection precision of the abnormal heat loss area is improved.

The invention belongs to the technical field of image recognition, and particularly relates to a garbage classification method and system based on image enhancement, and the method comprises the steps: carrying out the preprocessing of an obtained garbage image, and sampling a reference region block according to a local color variance, so as to extract a reference color model and a reference texture model; calculating a color consistency index, a texture homology index and a structure regularity index of each pixel point; determining a self-adaptive color weight and a self-adaptive texture weight according to a color statistical characteristic and a texture distribution characteristic in the reference region block; and based on the self-adaptive color weight, the self-adaptive texture weight and a preset structure weight, carrying out weighted fusion on the three indexes to generate a material authenticity map. According to the method, the enhancement effect on the complex stained junk image can be remarkably improved by fusing the multi-dimensional features and adopting the self-adaptive weight.

Embodiments of this application provide a method for generating a three-dimensional texture model of a city based on composite data, and a device. The method includes: obtaining satellite remote sensing data of a target region, where the satellite remote sensing data is used to determine a three-dimensional framework model and spatial coordinates of a to-be-measured object; obtaining video data within a preset range of the spatial coordinates of the to-be-measured object, where the video data is used to determine a surface texture of the to-be-measured object; and generating a three-dimensional texture model of the to-be-measured object based on the surface texture and the three-dimensional framework model of the to-be-measured object, where the three-dimensional texture model of the to-be-measured object is used to generate a three-dimensional texture model of the target region. The satellite remote sensing data and the video data within the preset range of the spatial coordinates of the to-be-measured object are obtained at low costs, so that costs and difficulty of constructing the three-dimensional texture model of the city are reduced, and costs and difficulty of updating the three-dimensional texture model of the city are reduced.

The application discloses a weak texture object pose estimation method based on deep learning and synthetic data, renders a non-texture CAD model to obtain synthetic data, obtains a pose potential feature extraction network through a self-encoder network composed of five convolution layers of a CBAM module, makes a codebook, inputs a real test image into an improved Mask R-CNN instance segmentation network to obtain bounding box information, performs cutting on the bounding box information, inputs the bounding box information into the pose potential feature extraction network to obtain a potential feature vector, calculates a cosine similarity according to the potential feature vector and a template potential vector, adopts a k nearest neighbor algorithm to obtain a 3D rotation pose, obtains a z-axis translation estimation value according to boundary box information of CAD model data in the real test image, adopts a conversion relationship between an image coordinate system and a camera coordinate system to obtain x-axis and y-axis translation amounts, improves the network expression feature capability, reduces the calculation complexity, and improves the 6D pose estimation recognition precision while reducing the model parameter amount.

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 present disclosure provides a method, apparatus, electronic device, and storage medium for generating a level of detail model. The method includes: performing a mesh simplification process on a target model to obtain a top-layer mesh model; determining a residual layer mesh model using a mesh refinement algorithm based on the top-layer mesh model and set model parameters, and recording the refinement parent-child relationship between the top-layer mesh model and the lower-layer mesh model; texture mapping the bottom-layer mesh model to obtain a bottom-layer texture model; and determining a level of detail model based on the mesh refinement parent-child relationship and the bottom-layer texture model. The level of detail model constructed in the present disclosure has a texture map with consistent geometric precision and hierarchically sharpened texture details, and the consistent geometric precision can avoid "crack" defects.

The application relates to a crowd-sourced unordered image auxiliary-based urban scene reconstruction method and device and a storage medium, wherein the method comprises the following steps: acquiring laser radar data to form basic point cloud data; acquiring crowd-sourced unordered images, estimating a depth map by using a depth estimation network, and obtaining auxiliary point cloud data; fusing the basic point cloud data and the auxiliary point cloud data to obtain geometric information; acquiring multispectral images and panchromatic images obtained by satellite remote sensing, determining ground object color information based on the multispectral images, and determining a ground surface texture model and a ground object model based on the panchromatic images; performing data preprocessing on the crowd-sourced unordered images to obtain a texture image, combining the ground object model and the ground object color information to obtain a ground object texture model; determining texture information based on the ground object texture model and the ground surface texture model; and realizing urban scene reconstruction by using an automatic reconstruction solution based on the geometric information and the texture information. Compared with the prior art, the application has the advantages of high restoration degree and high reconstruction precision.