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48 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.
Method and system for improving Mura defect classification precision, electronic equipment and storage medium
InactiveCN119444739Areduce negative impactHigh precisionImage enhancementImage analysisGradationGrey level
The invention discloses a method and a system for improving Mura defect classification precision, electronic equipment and a storage medium, which are used for realizing accurate classification of Mura defects of low-gray-scale images. The method for improving the Mura defect classification precision comprises the steps of obtaining an image on a display screen to be detected; performing texture filtering processing on the image to obtain a filtered image; performing graying processing and defect segmentation processing on the filtered image to obtain a to-be-detected image; inputting the to-be-detected image into the Mura classification model to obtain a classification result; wherein the Mura classification model is obtained through the following training method: obtaining first data collected by a plurality of sensors; preprocessing the first data to obtain second data; extracting feature data in the second data; fusing the feature data according to an orthogonal sequence fusion algorithm to obtain a fusion result; and training according to a fusion result to obtain a Mura classification model.
Owner:SHENZHEN SEICHITECH TECHN CO LTD
Texture samplers and methods for implementing texture feedback
ActiveCN118096847BImprove feedback efficiencyincrease profitImage analysisProcessor architectures/configurationPattern recognitionComputer graphics (images)
Disclosed are a texture sampler and a method for implementing texture feedback. The texture sampler includes a texture address processing module and a texture filtering module. The texture address processing module samples at least one group of texels in a level of a texture associated with a pixel, each group of texels includes at least one texel. The texture address processing module transmits the level information and coordinate information of the at least one group of texels to the texture filtering module. The texture filtering module counts the area of the statistical block size in the texture space each time. The texture filtering module obtains the starting point of one group of the at least one group of texels each time when counting, so that the statistical block has the same starting point as the corresponding group of texels and the group of texels is included in the statistical block size. The texture filtering module performs at least one statistics, and each time the statistics returns which alignment block the starting point of the statistical block is located in and the position offset of the starting point of the statistical block relative to the starting point of the alignment block in which it is located. The alignment blocks are densely aligned in a manner of filling the texture space.
Owner:MOORE THREADS TECH CO LTD
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
Method for visualizing multi-radar detection range in space based on grid shader
The invention discloses a space multi-radar detection range visualization method based on a grid shader, and the method comprises the steps: calculating a probability detection range according to given radar parameters; according to a camera distance, a continuous change LOD is adaptively selected, and a multi-radar detection range in a point cloud form is drawn by using methods of edge detail enhancement, repeated texture filtering, adjacent voxel calculation and the like based on a GPU grid shader. Compared with a traditional drawing method, the method has the advantages that the detection probability distribution in the radar detection range can be expressed more accurately, and the detection range and the staggered range of each radar can still be expressed accurately when the detection ranges of multiple radars are overlapped. According to the GPU-based grid shader, the CPU operation load is liberated, the radar drawing efficiency is remarkably improved, the flexibility is higher, and the expandability is higher.
Owner:ZHEJIANG UNIV
Anisotropic texture filtering for sampling points in screen space
Texture filtering in computer graphics calculates first and second pairs of texture-space basis vectors that correspond to first and second pairs of screen-space basis vectors transformed to texture space under a local approximation of a mapping between screen space and texture space. Based on differences in magnitudes of the vectors of the pairs of texture-space basis vectors, an angular displacement is determined between a selected pair of the first and second pairs of screen-space basis vectors and screen-space principal axes of the local approximation of the mapping that indicate maximum and minimum scale factors of the mapping. The determined angular displacement and the selected pair of screen-space basis vectors are used to generate texture-space principal axes, with a major axis associated with the maximum scale factor of the mapping and a minor axis associated with the minimum scale factor of the mapping. A texture is filtered using the major and minor axes.
Owner:IMAGINATION TECH LTD
Skin microstructure texture filtering for runtime facial animations
A method of skin microstructure texture filtering for facial animation includes obtaining a plurality of one-dimensional (1D) filtered tiles corresponding to a plurality of filter axis angles and a plurality of filter parameters applied to a neutral tile, and at runtime, for each pixel representing a region of human skin, determining a principal direction of deformation, a principal filter parameter corresponding to the principal direction of deformation, and a secondary filter parameter corresponding to a secondary direction of deformation orthogonal to the principal direction of deformation, and selecting a first 1D filtered tile among the plurality of 1D filtered tiles, the first 1D filter tile corresponding to the secondary direction of deformation and the secondary filter parameter, and generating a respective two-dimensional (2D) filtered tile by convolving the first 1D filtered tile with a second 1D filter kernel corresponding to the principal direction of deformation and the principal filter parameter.
Owner:ELECTRONIC ARTS INC
Method and system for enhancing image quality
The invention discloses an image quality enhancement method and system, and relates to the technical field of image processing, and the method comprises the steps: extracting a brightness component of each frame from a video stream, and calculating a result image after the directional filtering of the brightness component through boundary filtering; calculating the image of the brightness component after the non-directional filtering; and calculating the overall gradient range of the image, and integrating the result image after the directional filtering and the image after the non-directional texture filtering through the overall gradient range. According to the method and the system disclosed by the invention, the image boundary is reserved, and the noise is also completely removed.
Owner:ZHEJIANG XINMAI SILICON CO LTD
A method and system for enhancing image quality
The present invention discloses a method and system for enhancing image quality, relating to the technical field of image processing. The method includes: extracting the luminance component of each frame from a video stream, calculating the resultant image after directional filtering of the luminance component through boundary filtering; calculating the image after non-directional filtering of the luminance component; calculating the overall gradient range of the image, and integrating the resultant image after directional filtering and the image after non-directional texture filtering through the overall gradient range. The method and system disclosed by the present invention enable the retention of image boundaries and the thorough removal of noise.
Owner:ZHEJIANG XINMAI SILICON 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
Texture filtering of texture represented by multilevel mipmap
Texture filtering is applied to a texture represented with a mipmap comprising a plurality of levels, wherein each level of the mipmap comprises an image representing the texture at a respective level of detail. A texture filtering unit has minimum and maximum limits on an amount by which it can alter the level of detail when it filters texels from an image of a single level of the mipmap. The range of level of detail between the minimum and maximum limits defines an intrinsic region of the texture filtering unit. If it is determined that a received input level of detail is in an intrinsic region of the texture filtering unit, texels are read from a single mipmap level of the mipmap, and the read texels from the single mipmap level are filtered to determine a filtered texture value representing part of the texture at the input level of detail. If it is determined that the received input level of detail is in an extrinsic region of the texture filtering unit: texels are read from two mipmap levels of the mipmap, and the read texels from the two mipmap levels are processed to determine a filtered texture value representing part of the texture at the input level of detail.
Owner:IMAGINATION TECH LTD
Image processing method for multidirectional edge texture filtering and related device
ActiveCN119151978Aimplement responseimprove accuracyImage enhancementImage analysisPattern recognitionImaging processing
The invention discloses an image processing method for multidirectional edge texture filtering and a related device, which are used for improving the accuracy and robustness of edge detection. The method comprises the following steps: acquiring a shot image of a display screen to be detected, and performing corner correction on the shot image to obtain a standard image; a target weight sequence is generated based on the filtering step length parameter and a sine function, the target weight sequence is selectively inverted according to an input polarity parameter, the polarity parameter is used for controlling edge detection directions, and the edge detection directions comprise positive polarity edge detection, negative polarity edge detection and bipolar edge detection; a target convolution kernel is constructed according to input direction parameters, polarity parameters and a target weight sequence, the direction parameters are used for controlling filtering directions, and the filtering directions comprise column direction filtering, row direction filtering and multi-direction filtering; and performing convolution operation on the standard image based on the target convolution kernel, and integrating the output images according to the polarity parameter and the direction parameter to obtain a target image.
Owner:SHENZHEN SEICHITECH TECHN CO LTD
A method and device for detecting sizing reed collision based on image processing
The present invention discloses a method and device for detecting sizing yarn hitting the reed based on image processing, which belongs to the field of sizing yarn, and includes: pre-processing the sizing yarn hitting the reed image to be tested by main direction texture filtering, setting irrelevant pixels to zero, and obtaining the region of interest; extracting the deep feature map by multi-scale fusion of the reed coil features by feature extraction neural network, and obtaining the reed coil structure enhanced feature map by the reed coil structure information attention mechanism; inputting it into the detection frame generation neural network to generate an elliptical detection frame, and obtaining the detection result according to the elliptical detection frame. The main direction texture filtering is used to effectively remove the background noise of the sizing yarn hitting the reed image to be tested, and the feature extraction neural network and the reed coil structure information attention mechanism are combined to extract features, and then an elliptical detection frame is generated to perform sizing yarn hitting the reed detection, which effectively avoids noise interference, improves the detection accuracy of sizing yarn hitting the reed, and can stably perform detection in complex environments, with strong robustness.
Owner:JIANGNAN UNIV
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
Texture filtering of texture represented by multilevel mipmap
Texture filtering is applied to a texture represented with a mipmap comprising a plurality of levels, wherein each level of the mipmap comprises an image representing the texture at a respective level of detail. A texture filtering unit has minimum and maximum limits on an amount by which it can alter the level of detail when it filters texels from an image of a single level of the mipmap. The range of level of detail between the minimum and maximum limits defines an intrinsic region of the texture filtering unit. If it is determined that a received input level of detail is in an intrinsic region of the texture filtering unit, texels are read from a single mipmap level of the mipmap, and the read texels from the single mipmap level are filtered to determine a filtered texture value representing part of the texture at the input level of detail. If it is determined that the received input level of detail is in an extrinsic region of the texture filtering unit: texels are read from two mipmap levels of the mipmap, and the read texels from the two mipmap levels are processed to determine a filtered texture value representing part of the texture at the input level of detail.
Owner:IMAGINATION TECH LTD
Anisotropic texture filtering using adaptive filter kernel
A texture filtering unit applies anisotropic filtering using a filter kernel which can be adapted to apply different amounts of anisotropy up to a maximum amount of anisotropy. If it is determined that a received input amount of anisotropy is not above the maximum amount of anisotropy, the filter kernel applies the input amount of anisotropy, and texels of a texture are sampled using the filter kernel to determine a filtered texture value. If it is determined that the input amount of anisotropy is above the maximum amount of anisotropy, the filter kernel applies an amount of anisotropy that is not above the maximum amount of anisotropy, a plurality of sampling operations are performed to sample texels of the texture using the filter kernel to determine a respective plurality of intermediate filtered texture values, and the plurality of intermediate filtered texture values are combined to determine a filtered texture value which has been filtered in accordance with the input amount of anisotropy and the input direction of anisotropy.
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
An image processing method and related device for multi-directional edge texture filtering
ActiveCN119151978Bcontainment leakimplement responseImage enhancementImage analysisImaging processingRadiology
The present application discloses an image processing method and related device for multi-directional edge texture filtering, which are used to improve the accuracy and robustness of edge detection. The present application method includes: obtaining a captured image of a display screen to be detected, and performing corner correction on the captured image to obtain a standard image; generating a target weight sequence based on a filtering step size parameter and a sine function, and selectively reversing the target weight sequence according to an input polarity parameter, the polarity parameter is used to control the edge detection direction, and the edge detection direction includes positive polarity edge detection, negative polarity edge detection, and bipolar edge detection; constructing a target convolution kernel according to the input direction parameter, polarity parameter, and target weight sequence, the direction parameter is used to control the filtering direction, and the filtering direction includes column direction filtering, row direction filtering, and multi-directional filtering; performing a convolution operation on the standard image based on the target convolution kernel, and integrating the output image according to the polarity parameter and the direction parameter to obtain the target image.
Owner:SHENZHEN SEICHITECH TECHN CO 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
Global sparse texture filtering method based on edge structure preservation
ActiveCN119417721Aquality improvementImprove smoothnessImage enhancementImage analysisThresholdingNon convex optimization
The invention provides a global sparse texture filtering method based on edge structure preservation, which comprises the following steps of: introducing a texture suppression function into a penalty term, constraining the gradient of an output image, and suppressing texture, noise and unnecessary detail information in the image by setting two threshold values through the texture suppression function, then, the suppressed gradient is used as input of a denominator of a penalty term, so that the penalty term can fully distinguish the texture from the structure; a penalty term is constrained by adopting a sparse regular L1 norm, non-convex optimization is converted into convex optimization by introducing subgradient, and iterative solution is carried out by adopting an alternating direction multiplier method, so that better edge preservation is achieved; and an Lp norm with relatively high sparsity is adopted to constrain a penalty term, and a preprocessing conjugate gradient method is adopted to accelerate and improve the calculation efficiency, so that a more robust and sparsity image smoothing effect is achieved. According to the method, the robustness of an algorithm for distinguishing textures and structures can be improved, relatively good semantic information is reserved, and relatively good smooth performance of edge structure maintenance is achieved.
Owner:CHONGQING UNIV OF TECH
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