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12 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.
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
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
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
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