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24 results about "Texture filtering" patented technology
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In computer graphics, texture filtering or texture smoothing is the method used to determine the texture color for a texture mapped pixel, using the colors of nearby texels (pixels of the texture). There are two main categories of texture filtering, magnification filtering and minification filtering. Depending on the situation texture filtering is either a type of reconstruction filter where sparse data is interpolated to fill gaps (magnification), or a type of anti-aliasing (AA), where texture samples exist at a higher frequency than required for the sample frequency needed for texture fill (minification). Put simply, filtering describes how a texture is applied at many different shapes, size, angles and scales. Depending on the chosen filter algorithm the result will show varying degrees of blurriness, detail, spatial aliasing, temporal aliasing and blocking. Depending on the circumstances filtering can be performed in software (such as a software rendering package) or in hardware for real time or GPU accelerated rendering or in a mixture of both. For most common interactive graphical applications modern texture filtering is performed by dedicated hardware which optimizes memory access through memory cacheing and pre-fetch and implements a selection of algorithms available to the user and developer.
Adhesive tape pasting position positioning method, control device and equipment
The embodiment of the invention provides an adhesive tape pasting position positioning method, a control device and equipment, and the method comprises the steps: S1, collecting a to-be-pasted region picture, and making a data set; s2, using the data set to train and improve a NanoDet model, and obtaining a detection model, the improvement measures including texture filter and feature enhancement; s3, detecting an input to-be-pasted area by using the detection model, and obtaining position information of the to-be-pasted area; and S4, adhesive tape pasting is carried out according to the position information. The method can adapt to different working scenes, the positioning accuracy of the adhesive tape pasting position is improved, and the product quality is improved.
Owner:HELITE (KUNSHAN) PACKAGING MATERIALS CO LTD
Techniques for stochastic texture filtering through single instruction multiple threads and single instruction multiple data lane communication
The disclosed method for rendering graphic images includes, for each lane contained in a plurality of lanes in a wave, sampling a texel based on a filter to generate a texel sample; for each lane contained in the plurality of lanes, computing a filtered value based on a plurality of the texel samples read from a corresponding plurality of lanes, based on a footprint associated with the lane; and rendering at least one section of a graphic image based on the filtered values computed for the plurality of lanes.
Owner:NVIDIA CORP
Anisotropic Texture Filtering Using Weights of an Anisotropic Filter that Minimize a Cost Function
PendingUS20250363714A1Image analysisComplex mathematical operationsComputational physicsTexture filtering
A method of performing anisotropic texture filtering involves performing isotropic filtering at each sampling point of a set of sampling points in an ellipse to produce isotropic filter results. Weights of an anisotropic filter are selected that minimize a cost function that penalises high frequencies in the filter response of the anisotropic filter under a constraint that the variance of the anisotropic filter is related to an anisotropic ratio squared, the anisotropic ratio being the ratio of a major radius of the ellipse to be sampled and a minor radius of the ellipse to be sampled. The plurality of isotropic filter results are combined using the selected weights of the anisotropic filter to generate at least a portion of a filter result.
Owner:IMAGINATION TECH LTD
Anisotropic Texture Filtering by Combining Results of Isotropic Filtering at a Plurality of Sampling Points with a Gaussian Filter
A method of performing anisotropic texture filtering includes generating one or more parameters describing an elliptical footprint in texture space; performing isotropic filtering at each of a plurality of sampling points along a major axis of the elliptical footprint, wherein a spacing between adjacent sampling points of the plurality of sampling points is proportional to √{square root over (1−η−2)} units, wherein η is a ratio of a major radius of an ellipse to be sampled and a minor radius of the ellipse to be sampled, wherein the ellipse to be sampled is based on the elliptical footprint; and combining results of the isotropic filtering at the plurality of sampling points with a Gaussian filter to generate at least a portion of a filter result.
Owner:IMAGINATION TECH LTD
Anisotropic texture filtering by combining isotropic filtering results at each of a plurality of sampling points
A method of performing anisotropic texture filtering includes generating one or more parameters describing an elliptical footprint in texture space; performing isotropic filtering at each of a plurality of sampling points in an ellipse to be sampled, the ellipse to be sampled based on the elliptical footprint; and combining results of the isotropic filtering at each of the plurality of sampling points to generate a combination result by a sequence of linear interpolations, wherein each linear interpolation in the sequence of linear interpolations comprises blending a result of a previous linear interpolation in the sequence with the isotropic filtering results for one or more of the plurality of sampling points, the one or more of the plurality of sampling points for a linear interpolation being closer to a midpoint of the major axis of the elliptical footprint than the one or more of the plurality of sampling points for the previous linear interpolation in the sequence.
Owner:IMAGINATION TECH LTD
Charging pile oil vehicle occupation prevention intelligent monitoring management system based on Internet of Things video monitoring
PendingCN121963107ASolving the problem of broken driving tracksavoid idlingCharacter and pattern recognitionClosed circuit television systemsVideo monitoringBodies oil
The invention belongs to the technical field of internet-of-things video monitoring, and provides an intelligent monitoring management system for preventing oil vehicle occupation of a charging pile based on internet-of-things video monitoring, and the system comprises an intelligent monitoring management module which is used for solving the problems of trajectory tracking interruption and occupation prevention failure caused by similar characteristics of a snowfield and a white vehicle body oil vehicle in snowy weather. The method comprises the following steps: acquiring a video through a camera, judging track breakage, analyzing snowfield and vehicle body feature interference, improving the discrimination degree by means of brightness adjustment, LBP texture filtering, shadow and edge enhancement and the like, reconstructing a vehicle body contour by fusing multiple features, and recovering a breakage track through datum point positioning, motion parameter calculation and inertia completion. And finally, inputting an AI model to predict a driving intention and linking a parking lock. The anti-occupation technical bottleneck in special weather is effectively solved, the accuracy and stability of monitoring control are improved, and efficient utilization of the charging pile is guaranteed.
Owner:HANGZHOU FANXI TECHNOLOGY CO LTD
A high-performance modular differentiable rendering method, apparatus, device, and storage medium based on DCU
This invention relates to the field of computer graphics technology, specifically to a high-performance modular differentiable rendering method, apparatus, device, and storage medium based on a Digital Core Utility (DCU). The method includes: initializing a modular rendering pipeline; placing rendering data in DCU memory in tensor form; performing geometric transformations on vertex coordinates and outputting homogeneous coordinates, with delayed perspective division; generating pixel-level geometric information through a multi-layered pipeline of triangle setup, layered binning, coarse rasterization, and fine rasterization; and performing rendering and gradient backpropagation through a pluggable, differentiable modular rendering pipeline composed of rasterization, attribute interpolation, texture filtering, anti-aliasing, and shading modules. This invention deeply optimizes the domestic DCU architecture, solving the problems of difficult porting, poor non-modular scalability, and coarse gradient approximation in existing technologies. It achieves high-performance, high-precision differentiable rendering on domestic hardware, significantly improving rendering speed, system flexibility, and 3D reconstruction convergence.
Owner:CHONGQING BITMAP INFORMATION TECH CO LTD
Insulation defect identification method, system and equipment of power equipment and medium
PendingCN121959165AAutomate the processRealize multi-dimensional feature extractionTesting dielectric strengthAlgorithmFrequency filtering
The invention provides an insulation defect identification method, system and device for power equipment and a medium, and the method comprises the steps: obtaining a partial discharge signal, and extracting a multi-resolution time-frequency feature of the partial discharge signal through a preset window width through Gabor transformation; inputting the multi-resolution time-frequency characteristics into a pre-trained insulation defect identification model, and obtaining an identification result of the insulation defect type of the power equipment based on the output of the insulation defect identification model; wherein the insulation defect identification model is configured with a frequency filter, a time filter and a texture filter, the frequency filter, the time filter and the texture filter respectively adopt convolution kernels of different sizes, and the frequency filter, the time filter and the texture filter are respectively used for processing time-frequency characteristics of different resolutions. According to the invention, the accuracy of insulation defect type identification can be improved.
Owner:GUANGDONG YANGJIANG CHUANGYUAN OFFSHORE WIND POWER COMPREHENSIVE INVESTMENT CO LTD +3
A global sparse texture filtering method based on edge structure preservation
The application provides a global sparse texture filtering method based on edge structure preservation, including introducing a texture inhibition function in a penalty term, and constraining the gradient of an output image, the texture inhibition function inhibits texture, noise and unnecessary detail information in the image by setting two threshold values, then using the inhibited gradient as the input of the denominator of the penalty term, so that the penalty term can sufficiently distinguish texture and structure; sparse regular L1 norm is used to constrain the penalty term, non-convex optimization is converted into a convex optimization problem by introducing a sub-gradient, and an alternating direction multiplier method is used for iterative solution, so that better edge preservation is achieved; sparse L p Norm is used to constrain the penalty term and a preconditioned conjugate gradient method is used to accelerate and improve the calculation efficiency, so that more robust and sparse image smoothing effect is achieved. The application can improve the robustness of the algorithm in distinguishing texture and structure, retain better semantic information, and achieve better edge structure preservation and smoothing performance.
Owner:CHONGQING UNIV OF TECH
Techniques for stochastic texture filtering through single instruction multiple threads and single instruction multiple data lane communication
PendingDE102025130602A1Processor architectures/configuration3D-image renderingComputer graphics (images)Algorithm
The disclosed method for rendering graphic images includes, for each lane contained in a plurality of lanes in a wave, sampling a texel based on a filter to generate a texel sample; for each lane contained in the plurality of lanes, computing a filtered value based on a plurality of the texel samples read from a corresponding plurality of lanes, based on a footprint associated with the lane; and rendering at least one section of a graphic image based on the filtered values computed for the plurality of lanes.
Owner:NVIDIA CORP
Techniques for stochastic texture filtering through single-instruction, multiple threads and single instruction, multiple data lane communication
The disclosed method for rendering graphics images includes, for each lane included in a plurality of lanes in a wave, sampling a texel based on a filter to generate a texel sample; for each lane included in the plurality of lanes, computing a filtered value based on a plurality of the texel samples that are read from a corresponding plurality of lanes based on a footprint associated with the lane; and rendering at least one portion of a graphics image based on the filtered values computed for the plurality of lanes.
Owner:NVIDIA CORP
Texture filtering test method and device, equipment and medium
The invention provides a texture filtering testing method and device, equipment and a medium, and belongs to the technical field of testing. The texture filtering test method comprises the following steps: determining a combined filtering mode according to a plurality of single filtering modes supported by a to-be-tested unit; determining combined counting information corresponding to the combined filtering mode according to target texel attribute information corresponding to the to-be-detected texture data and single counting information of each single filtering mode corresponding to the combined filtering mode; according to the combined filtering mode, the combined counting information and the to-be-detected texture data, obtaining input data of the to-be-detected unit; the to-be-tested unit is tested based on the input data, a test result of the to-be-tested unit is obtained, and the test result is used for representing whether the to-be-tested unit passes the texture filtering test or not. According to the embodiment of the invention, the number of test cases can be reduced, and the test efficiency is improved.
Owner:MOORE THREADS TECHNOLOGY (CHENGDU) CO LTD
Image fusion method and device, image processing apparatus and storage medium
The application discloses an image fusion method and device, an image processing device and a storage medium. The image fusion method comprises the following steps: decomposing a source image into a base layer and a detail layer through a bilateral texture filter, wherein the source image comprises an infrared image and a visible light image; fusing the base layer of the source image based on a saliency detection algorithm to obtain a first fused image; fusing the detail layer of the source image based on visual fidelity to obtain a second fused image; and fusing the first fused image and the second fused image to obtain a fused image of the source image. The above technical scheme fuses the base layer of the source image through the saliency detection algorithm, fuses the detail layer through the visual fidelity, retains the contrast information and texture of the source image, avoids the generation of a pseudo-edge phenomenon, and improves the definition of the fusion of the infrared image and the visible light image.
Owner:CHINA FAW CO LTD
System and method for adjusting filtering for texture streaming
A technique for texture filtering. A transition is made from a first mipmap corresponding to a first texture resolution to a second mipmap corresponding to a second texture resolution. The first texture resolution is lower than the second texture resolution. As compared to standard trilinear filtering, initiation of the transition is delayed by an offset (or bias), which serves to delay the initial use of the second mipmap until it has been loaded. Following initiation of the transition, first and second weightings are selected with a nonlinear filter, and the system interpolates between the first mipmap and the second mipmap by applying the weightings. During an initial portion of the transition, the nonlinear filter has a slope that is higher than that of the standard trilinear filter.
Owner:ADVANCED MICRO DEVICES INC +1
Techniques for stochastic texture filtering through single-instruction, multiple threads and single instruction, multiple data lane communication
The disclosed method for rendering graphics images includes, for each lane included in a plurality of lanes in a wave, sampling a texel based on a filter to generate a texel sample; for each lane included in the plurality of lanes, computing a filtered value based on a plurality of the texel samples that are read from a corresponding plurality of lanes based on a footprint associated with the lane; and rendering at least one portion of a graphics image based on the filtered values computed for the plurality of lanes.
Owner:NVIDIA CORP
Anisotropic texture filtering using weights of an anisotropic filter that minimize a cost function
A method of performing anisotropic texture filtering includes generating one or more parameters describing an elliptical footprint in texture space; performing isotropic filtering at each sampling point of a set of sampling points in an ellipse to be sampled to produce a plurality of isotropic filter results, the ellipse to be sampled based on the elliptical footprint; selecting, based on one or more parameters of the set of sampling points and one or more parameters of the ellipse to be sampled, weights of an anisotropic filter that minimize a cost function that penalises high frequencies in the filter response of the anisotropic filter under a constraint that the variance of the anisotropic filter is related to an anisotropic ratio squared, the anisotropic ratio being the ratio of a major radius of the ellipse to be sampled and a minor axis of the ellipse to be sampled; and combining the plurality of isotropic filter results using the selected weights of the anisotropic filter to generate at least a portion of a filter result.
Owner:IMAGINATION TECH LTD
A method for multi-radar detection range visualization in space based on grid shader
The application discloses a multi-radar detection range visualization method in space based on a grid shader, which comprises the following steps: calculating the probability detection range according to given radar parameters; adaptively selecting a continuously changing LOD according to the camera distance; using edge detail enhancement, repeated texture filtering, adjacent voxel calculation and other methods based on a GPU grid shader to draw the multi-radar detection range in the form of a point cloud. Compared with a traditional drawing method, the application can more accurately express the detection probability distribution in the radar detection range, and can still accurately express the detection range and the staggered range of each radar when the multi-radar detection ranges overlap. Moreover, the application is based on the GPU grid shader, which releases the CPU operation load, significantly improves the drawing efficiency of the radar, is higher in flexibility and stronger in scalability.
Owner:ZHEJIANG UNIV
Anisotropic texture filtering by combining results of isotropic filtering at a plurality of sampling points with a gaussian filter
A method of performing anisotropic texture filtering includes generating one or more parameters describing an elliptical footprint in texture space; performing isotropic filtering at each of a plurality of sampling points along a major axis of the elliptical footprint, wherein a spacing between adjacent sampling points of the plurality of sampling points is proportional to √{square root over (1−η−2)} units, wherein η is a ratio of a major radius of an ellipse to be sampled and a minor radius of the ellipse to be sampled, wherein the ellipse to be sampled is based on the elliptical footprint; and combining results of the isotropic filtering at the plurality of sampling points with a Gaussian filter to generate at least a portion of a filter result.
Owner:IMAGINATION TECH LTD
Input / Output Filter Unit for Graphics Processing Unit
Input / output filter units for use in a graphics processing unit include a first buffer configured to store data received from, and output to, a first component of the graphics processing unit; a second buffer configured to store data received from, and output to, a second component of the graphics processing unit; a weight buffer configured to store filter weights; a filter bank configurable to perform a plurality of types of filtering on a set of input data, the plurality of types of filtering comprising texture filtering types and pixel filtering types; and control logic configured to cause the filter bank to: (i) perform one of the plurality of types of filtering on a set of data stored in one of the first and second buffers using a set of weights stored, and (ii) store the results of the filtering in one of the first and second buffers.
Owner:IMAGINATION TECH LTD
A crystal flower detection method, device, electronic device and storage medium
The present invention provides a crystal flower detection method, device, electronic device and storage medium. The method includes: obtaining multiple crystal flower images to be tested and detection light source-camera optical axis angle information of a coated metal plate to be tested, performing a first image reconstruction based on the detection light source-camera optical axis angle information and the multiple crystal flower images to be tested to obtain a reflectivity information image to be tested, performing detection convolution on the reflectivity information image to be tested with multiple preset two-dimensional texture filters to obtain multiple target feature texture vector images, inputting all the target feature texture vector images into a crystal flower classification model to perform crystal flower size classification to obtain a target crystal flower size category, calculating energy feature quantities of multiple grayscale co-occurrence matrices of the reflectivity information image to be tested based on multiple preset scanning directions, obtaining a feature energy mean value based on all the energy feature quantities to determine the uniformity of crystal flower texture distribution; and improving the accuracy and intelligent efficiency of crystal flower detection by the target crystal flower size category and the uniformity of crystal flower texture distribution.
Owner:CISDI RES & DEV CO LTD
Stochastic texture filtering
Stochastic texture filtering introduces randomness into texel sampling and / or filtering. Instead of computing a closest texel for the texture coordinates, randomness is introduced by stochastic sampling to obtain one texel. Stochastic sampling is also applied for filtering the texels when multiple samples are used and / or to perform temporal filtering. A first technique is used for discrete filters and filter-specific sample weights are generated. In contrast with conventional techniques, the sample weights are not applied directly to the single texel value. The single texel is randomly selected for each pixel, with probability proportional to an associated sample weight. A second technique is used for continuous filters and weights are not generated. Instead, the texture coordinates are perturbed with a random offset, which is drawn from a filter-specific probability distribution. Stochastic texture filtering improves the performance of texture filtering in terms of speed and quality and is compatible with image reconstruction techniques.
Owner:NVIDIA CORP
A method, system, medium and device for identifying a semi-loaded state vehicle-mounted scrap steel main body target
The application discloses a kind of semi-load state vehicle-mounted scrap steel main body target identification method, system, medium and equipment, comprising: obtaining the image to be identified of vehicle-mounted scrap steel;The target region image is obtained by identifying to the image to be identified;The target region image is carried out background texture filtering processing;Edge extraction is carried out to the target region image after background texture filtering processing, and edge extraction image is obtained;Contour detection is carried out to the edge extraction image, and a plurality of complete closed contour blocks are obtained;Characteristic value extraction is carried out to each contour block, and the characteristic value of each contour block is obtained;Characteristic value fusion method is used to fuse the characteristic value of each contour block, and the mutually different characteristic value is obtained, and the number of mutually different characteristic value is used as adaptive classification number;According to adaptive classification number, the image to be identified is carried out background segmentation, and accurate scrap steel main body target image is obtained.The application effectively improves the identification precision of target main body.
Owner:ANQING NORMAL UNIV
Stochastic texture filtering
Stochastic texture filtering introduces randomness into texel sampling and / or filtering. Instead of computing a closest texel for the texture coordinates, randomness is introduced by stochastic sampling to obtain one texel. Stochastic sampling is also applied for filtering the texels when multiple samples are used and / or to perform temporal filtering. A first technique is used for discrete filters and filter-specific sample weights are generated. In contrast with conventional techniques, the sample weights are not applied directly to the single texel value. The single texel is randomly selected for each pixel, with probability proportional to an associated sample weight. A second technique is used for continuous filters and weights are not generated. Instead, the texture coordinates are perturbed with a random offset, which is drawn from a filter-specific probability distribution. Stochastic texture filtering improves the performance of texture filtering in terms of speed and quality and is compatible with image reconstruction techniques.
Owner:NVIDIA CORP