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36 results about "Texture Descriptor" patented technology
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Characteristic of the surface or consistency of a finding or feature.
Methods and Apparatus for Visual Search
ActiveUS20130121600A1Digital data information retrievalCharacter and pattern recognitionPattern recognitionBag of features
Each image of a set of images is characterized with a set of sparse feature descriptors and a set of dense feature descriptors. In some embodiments, both the set of sparse feature descriptors and the set of dense feature descriptors are calculated based on a fixed rotation for computing texture descriptors, while color descriptors are rotation invariant. In some embodiments, the descriptors of both sparse and dense features are then quantized into visual words. Each database image is represented by a feature index including the visual words computed from both sparse and dense features. A query image is characterized with the visual words computed from both sparse and dense features of the query image. A rotated local Bag-of-Features (BoF) operation is performed upon a set of rotated query images against the set of database images. Each of the set of images is ranked based on the rotated local Bag-of-Features operation.
Owner:ADOBE SYST INC
Systems and Methods for Managing Texture Descriptors in a Shared Texture Engine
ActiveUS20090128575A1Memory adressing/allocation/relocationDetails involving image processing hardwarePattern recognitionData buffer
Provided are methods for managing texture data. The methods include preloading a first plurality of texture descriptor values from a memory location in a first buffer located in a first logic block, wherein the first buffer is further configured to receive data corresponding to non-texture functions performed in the first logic block and preloading the first plurality of texture descriptor values from a memory location into a second buffer in a second logic block if the first buffer is full. The methods further include utilizing the first plurality of texture descriptor values, within the second logic block, to perform a shader calculation, and loading, dynamically, a second plurality of texture descriptor values from memory into the first buffer, wherein the first logic block requires additional data. Additionally, the methods can include writing, if the first buffer is full, the second plurality of texture descriptor values over a portion of the first plurality of texture descriptor values.
Owner:VIA TECH INC
Remote sensing image processing method combined with shape self-adaption neighborhood and texture feature extraction
InactiveCN103208001AImprove classification accuracySolve the normalization problemCharacter and pattern recognitionTexture extractionStorage area network
The invention discloses a remote sensing image processing method combined with the shape self-adaption neighborhood and the texture feature extraction for image preprocessing. The method includes subjecting compressed image to a gray level co-occurrence matrix calculation; subjecting the generated gray level co-occurrence matrix to S coefficient modification of an SAN (Storage Area Networking) irregular object window to obtain a regular matrix; calculating a new co-occurrence matrix according to the modified regular matrix and selecting texture descriptors with obvious feature and low correlation; extracting texture feature map in the SAN irregular images; and calculating to obtain accurate images with combination feature which is overall comprehensive feature of neighborhood. According to the method, the overall classification accuracy based on a shape self-adaption neighborhood method can be improved by 4%. The method can not only extract the texture feature in the SAN irregular images of remote sensing images completely, but also process the extraction of mixed pixel feature of the fuzzy edge of earth surface objects, and is applicable to texture extraction of earth surface objects in natural states.
Owner:SOUTH CHINA NORMAL UNIVERSITY
Cable defect detection optimization method based on gray scale run matrix
The invention discloses a cable defect detection optimization method based on a gray scale run matrix, and relates to the field of artificial intelligence. Comprising the steps of obtaining multiple regional images; calculating a roughness coefficient of each pixel point in each regional image according to each pixel point in each regional image and gray levels of pixel points in eight neighborhoods of each pixel point, further obtaining a roughness coefficient level image, and obtaining a gray level run length matrix corresponding to each regional image; calculating the roughness degree of each region image in each direction; the pixel points not smaller than the run threshold are marked in the direction with the maximum roughness degree, all marked areas are obtained, and the marked areas are combined to obtain a rough area; and judging each obtained rough region to obtain all cable defect regions. The texture features of the weak unsmooth area are extracted based on the improved gray scale run matrix, a better extraction effect can be obtained compared with a conventional texture descriptor, and the defect detection precision is effectively improved.
Owner:江苏奥派电气科技有限公司
Methods and Apparatus for Visual Search
ActiveUS20130121570A1Digital data information retrievalCharacter and pattern recognitionPattern recognitionBag of features
For each image of a set of images, the each image is characterized with a set of fixed-orientation texture descriptors and a set of color descriptors. The set of images is indexed in a color index and a texture index. Similarly, a query image is characterized with a set of fixed-orientation texture descriptors. The set of fixed orientation texture descriptors of the query image includes a set of fixed orientation descriptors for each of a set of rotated query images, and a set of color descriptors of the query image. A rotated local Bag-of-Features (BoF) operation is performed upon the set of rotated query images and the set of images. Each of the set of images is ranked based on the rotated local Bag-of-Features operation.
Owner:ADOBE SYST INC
Image processing method for detecting pulmonary tuberculosis focus in chest X-ray DR film
InactiveCN105139390AReduce workloadImprove recognition accuracyImage enhancementImage analysisPattern recognitionCluster algorithm
The invention provides an image processing method for detecting a pulmonary tuberculosis focus in a chest X-ray DR film. The image processing method comprises the following steps of extracting a lung area image through an active shape model; extracting SURF key feature points of a lung area image; extracting texture descriptors of the key feature points; assigning the kind of the key feature points; performing clustering and combination on the texture descriptors according to a k-means clustering algorithm, and performing classified making on a combination result; setting a group of dimension values, and combining the texture marks of the lung area image according to a sequence from a least dimension value to a minimum dimension value; and deleting the class of which the number of samples is smaller than 5% of the total number in the combining result, thereby only reserving main classes in the combination result, eliminating abnormities and obtaining an image processing result. The image processing method can be used for automatically processing the DR films. The image processing method is applied for screening the pulmonary tuberculosis. The lung DR films which may represent the pulmonary tuberculosis can be screened. The image processing method can be used for large-number pulmonary tuberculosis screening. The image processing method has advantages of high screening efficiency and objective and stable conclusion.
Owner:SICHUAN UNIV
Dividing method based on CT image liver tumor focus
ActiveCN105488781AImprove timelinessAvoid the risk of failure due to initial randomizationImage enhancementImage analysisPattern recognitionImaging processing
The invention is applicable to the field of medical image processing, and provides a dividing method based on the CT (Computed Tomography) image liver tumor focus. The method comprises the following steps of preprocessing an initial CT image; completing ROI selection on the suspected focus in an interactive way by aiming at the preprocessed CT image; performing texture description on the ROI based on a CT value, and obtaining probability spectrums of the ROI through weighted computation of texture descriptors; building a big data priori knowledge base, determining a focus region probability spectrum threshold value, and dividing the suspected focus based on the threshold value; and completing the volume statistics and the quantization output on the focus. The method has the advantages that a plurality of texture descriptor probability spectrums are obtained based on stable and comparable CT value calculation; meanwhile, the threshold value of the texture descriptor probability spectrums is obtained through manual division calculation based on the priori knowledge base; and the accuracy and the timeliness of the multi-domain liver tumor focus division in the CT image can be realized.
Owner:THE SECOND PEOPLES HOSPITAL OF SHENZHEN
Texture description method and texture-based image retrieval method using Gabor filter in frequency domain
InactiveUS6870965B2Reduce computing timeEasy retrievalImage analysisData processing applicationsTime domainImage retrieval
A texture description method in a frequency domain for extracting texture features by transforming and Gabor-filtering an input image into an image of the frequency domain, and a texture-based retrieval method thereof are provided. The texture description method in the frequency domain includes: a first step of transforming an image of a time domain into an image of the frequency domain; a second step of filtering the transformed image using a Gabor filter having N×M filter regions, where N and M are respective predetermined positive integers; a third step of extracting feature values of the image that has been Gabor-filtered in respective channels of the frequency domain division layout corresponding to the N×M filter regions of the Gabor filter; and a fourth step of deciding a texture descriptor of the image on the basis of the feature values of the image.
Owner:SAMSUNG ELECTRONICS CO LTD +4
Method and apparatus for image texture describing
A method for retrieving an image texture descriptor for describing texture features of an image, including the steps of (a) filtering input images using predetermined filters having different orientation coefficients, (b) projecting the filtered images onto axes of each predetermined direction to obtain data groups consisting of averages of each directional pixel values, (c) selecting candidate data groups among the data groups by a predetermined classification method, (d) determining a plurality of indicators based on orientation coefficients of the filters used in filtering the candidate data groups, and (e) determining the plurality of indicators as the texture descriptor of the image. The texture descriptors which allow kinds of texture structure present in an image to be perceptually captured can be retrieved.
Owner:SAMSUNG ELECTRONICS CO LTD +1
One shot color calibrated metric learning for object re-identification
Techniques for detecting objects across images captured by camera devices. Embodiments capture, using first and second camera devices, first and second pluralities of images, respectively. First and second reference images are captured using the first and second camera devices. Color descriptors are extracted from the first plurality of images and the second plurality of images, and texture descriptors are extracted from the first plurality of images and the second plurality of images. Embodiments model a first color subspace and a second color subspace for the first camera device and the second camera device, respectively, based on the first and second pluralities of images and the first and second reference images. A data model for identifying objects appearing in images captured using the first and second camera devices is generated, based on the extracted color descriptors, texture descriptors and the first and second color subspaces.
Owner:DISNEY ENTERPRISES INC
Method for detecting natural scene image words
InactiveCN101436299AEfficient extractionEfficient determinationImage analysisCharacter and pattern recognitionText detectionContrast ratio
The invention discloses a method for detecting image characters in natural scene and relates to a method for adopting a texture descriptor LHBP to describe the texture character of an image and adopting a multi-dimension tropistic wave filtering method to detect characters in the image, thereby solving the problem that the character detection method based on texture has complex requirement on illumination; and the change of contrast gradient between the character and the background has great influence on detection. The obtained LHBP tropistic texture code and the corresponding code produced according to the change of position weight are obtained; a character region is determined through a multi-dimension tropistic analytic method. The method adopts a mode of extracting local texture on multi-dimension wavelet character by the LHBP texture descriptor, can filter out the influence of complexity and the change situation of the contrast ratio between the character and the background, effectively extracts the texture character of the character region, utilizes the texture direction property of the character region to determine the final character region and has good robustness in complex illumination, the change of the contrast gradient between the character and the background, the change of the size and the stroke thickness of the character and the like.
Owner:HARBIN INST OF TECH
Methods and apparatus for visual search
ActiveUS8781255B2Digital data information retrievalCharacter and pattern recognitionPattern recognitionBag of features
Each image of a set of images is characterized with a set of sparse feature descriptors and a set of dense feature descriptors. In some embodiments, both the set of sparse feature descriptors and the set of dense feature descriptors are calculated based on a fixed rotation for computing texture descriptors, while color descriptors are rotation invariant. In some embodiments, the descriptors of both sparse and dense features are then quantized into visual words. Each database image is represented by a feature index including the visual words computed from both sparse and dense features. A query image is characterized with the visual words computed from both sparse and dense features of the query image. A rotated local Bag-of-Features (BoF) operation is performed upon a set of rotated query images against the set of database images. Each of the set of images is ranked based on the rotated local Bag-of-Features operation.
Owner:ADOBE SYST INC
Identification or determination of a load based on texture
Owner:SMITHS HEIMANN
Method for detecting color difference degree of colored textile fabrics
InactiveCN110706294AUniversalAvoid the insufficiency of describing colors in a single color spaceImage analysisCharacter and pattern recognitionPattern recognitionColor analysis
The invention belongs to the technical field of image processing, and relates to a colored textile fabric color difference degree detection method which is widely applied to the fields of artificial intelligence, color analysis, pattern recognition, intelligent detection and the like. According to the method, color space conversion is firstly carried out on an image, then global color features andlocal texture descriptors are extracted from HSV and Lab color spaces respectively, and finally corresponding features are fused to obtain mixed color space color representation features. According to the method, the defect that colors are described in a single color space is overcome, the color depicting capacity of the mixed color space is improved, the color difference degree is accurately detected, and the method has universality for different colored spun fabrics.
Owner:WUHAN TEXTILE UNIV
Image matching method based on corner point and single-line segment marshalling feature description operator
ActiveCN110490913AReduce time complexitySmooth rotationImage analysisShortest distanceRotational invariance
The invention discloses an image matching method based on a corner point and single-line segment marshalling feature description operator. The method comprises the following steps: firstly, extractingline segments and Harris corner points from an image; constructing a line segment, searching and grouping the line segment, constructing an angular point-single line segment texture descriptor with scale, rotation and illumination invariance by using a detected angular point and the line segment, the Harris angular point having the advantage of rotation invariance, and the line segment adopting ahalf-width descriptor can improve the reliability in a parallax change scene; performing spatial weighted shortest distance measurement to obtain a local matching result; finally, determining the candidate matching of each line segment, establishing a matching matrix and solving a global matching result through spectral analysis. The image matching description operator has the characteristics ofscale, rotation and illumination invariance. According to the invention, image pyramids are respectively established for three-dimensional images. The pyramids on different layers are matched one byone, so that the scale influence can be eliminated. The defects of large marshalling calculation amount and long consumed time in multi-line-segment matching can be overcome.
Owner:HUAZHONG NORMAL UNIV
GoF/GoP texture description method, and texture-based GoF/GoP retrieval method and apparatus using the same
InactiveUS20050058195A1Reduce the amount requiredTelevision system detailsPicture reproducers using cathode ray tubesPattern recognitionGroup of pictures
A method of describing texture of a group of frames (GoF) or a group of pictures (GoP) using homogeneous texture descriptors, and a method and apparatus for retrieving a GoF / GoP using the texture description method are provided. The texture description method includes: generating homogeneous texture descriptors of all frames constituting the GoF or all pictures constituting the GoF; and expressing the GoF or GoP using a predetermined representative homogeneous texture descriptor corresponding to one frame or picture to reduce the amount of data. The GoF / GoP retrieval method includes: establishing a database of homogeneous texture descriptors of a plurality of GoFs's or GoPs's, each GoF or GoP being expressed by a predetermined representative homogeneous texture descriptor corresponding to one frame or picture to reduce the amount of data; generating a homogeneous texture descriptor corresponding to one frame or picture of a query GoF or GoP when the query GoF or GoP is input; searching homogeneous texture descriptors that are similar to the homogeneous texture descriptor of the query GoF or GoP in the database; and retrieving GoFs's or GoPs's corresponding to the searched similar homogeneous texture descriptors and arranging GoFs's or GoPs's in the order of degree of similarity. Therefore, the texture of images can be more accurately expressed, and an image can be more efficiently and rapidly retrieved.
Owner:SAMSUNG ELECTRONICS CO LTD
System and method for relating corresponding points in images with different viewing angles
A system, method and computer program product for relating corresponding points in images with an overlapping scene. An example method includes generating transformed images of a target image using different image transformations for each of transformed images. Texture descriptors are extracted for feature points in the transformed images and a reference image. Matched feature points are identified and inliers from matched feature points are selected. An aligning transformation is generated using the inliers for at least one of the transformed images. A panorama image is created with the target image and reference image after the images are aligned.
Owner:WAYFAIR LLC
Local texture description method based on local grouping comparison mode column diagram
InactiveCN103700119AImprove the ability to distinguishReduce complexityImage enhancementImage analysisSingle supportLocal Group
The invention provides a local texture description method based on a local grouping comparison mode column diagram. The method specifically comprises the following steps: step 1, selecting q supporting regions on the basis of an interest region; step 2, normalizing the supporting regions into circular regions; step 3, dividing circular images into P parts through convergence strategies based on mean value ordering; step 4, calculating a local grouping comparison mode of pixel coordinate points inside the circular images; step 5, counting a local grouping comparison mode column diagram in a local characteristic region according to the suffix of the local grouping comparison mode, so as to form local texture description of a single supporting region; step 6, cascading the local texture descriptions of the supporting regions to obtain a local grouping comparison mode column diagram for characteristic regions, and forming a local texture descriptor. According to the method, actually, a gray change column diagram for pixel point neighbourhood pixels with consistent gray values is calculated, and the descriptor formed by the method is strong in discrimination performance and has excellent robustness in illumination transformation and geometric transformation.
Owner:SHANGHAI JIAO TONG UNIV
Decomposition method of image texture and structure based on a total variational model of a guide map
InactiveCN109272539AGood texture/structure decompositionExact decompositionImage enhancementImage analysisImaging processingDecomposition
The invention discloses a decomposition method of image texture and structure based on a total variational model of a guide map, belonging to the technical field of image processing. According to theinvention, firstly, the local main structure of the image is reconstructed based on the guide image filter, then the texture descriptor is calculated according to the reconstructed local structure image, finally, the accuracy and computational efficiency of texture and structure decomposition are improved by combining the multi-scale total variational model and block translation method. The technical scheme of the invention can obtain better texture / structure decomposition effect for noisy images, the textures and structures of different scales can also be decomposed accurately, and the decomposed structure layers can keep the original shading of the image, avoiding the blurring of the structure caused by local smoothing or the color block effect caused by the global optimization method. In addition, the technical proposal of the invention needs to extract simple features, and does not depend on learning a large number of image samples.
Owner:YUNNAN UNIV
An image texture classification method based on jump subdivision local pattern
ActiveCN109271997AReduce feature dimensionCalculation speedCharacter and pattern recognitionLocal patternClassification methods
An image texture classification method based on jump subdivision local pattern includes:firstly, skipping local difference counting feature (JLDCP) information is extracted from the second-order difference counting feature and diagonal difference counting feature, then the subdivision complete local binary feature (RCLBP) is extracted from the symbol information and size information of the subdivision complete local binary feature, finally, the texture descriptors (JRLP) of skipping and subdivision local patterns are obtained by connecting the skipping local difference counting feature (JLDCP)and the subdivision complete local binary feature (RCLBP). The invention has the beneficial effects that the invention has robustness to image noise, rotation, scale and illumination change and the like.
Owner:HENAN UNIV OF SCI & TECH
Extraction method of histogram texture descriptor in muti-contrast mode
InactiveCN101894261ADescribe wellTexture features are efficient and simpleCharacter and pattern recognitionImage QuantificationHistogram
The invention provides an extraction method of a histogram texture descriptor in a muti-contrast mode, comprising the following steps: carrying out image quantization; separating the quantized image into a positive matrix, a negative matrix and an equivalent matrix; and carrying out histogram calculation according to the three matrixes to obtain the histogram descriptor in a local mode of the image. The technical proposal provided by the invention can achieve efficient and simple extraction of textural features and well express the texture of an SAR image.
Owner:WUHAN UNIV
System and method for managing grain data in computer
ActiveCN101236661AReduce redundancyLow costDetails involving image processing hardware3D-image renderingPattern recognitionLogic module
The present invention discloses a system and method for managing texture data in a computer, comprising the steps of: preloading a first plurality of texture descriptor values from a storage location in a first buffer located in a first logic module , in response to a read request, wherein the first buffer is used to receive data located in the first logical block; from the storage location, the first plurality of texture descriptor values are preloaded into a buffer located in a second logical block a second buffer; performing a shading calculation using the first plurality of texture descriptor values in the second logical module; dynamically loading the second plurality of texture descriptor values from memory into the first buffer, in response to the additional data requested by the first logic module; and overwriting a portion of the first plurality of texture descriptor values with the second plurality of texture descriptor values to reflect that the first buffer is full. The invention can reduce system redundancy and cost, improve texture processing operation, and improve operation.
Owner:VIA TECH INC
GoF/GoP texture description method, and texture-based GoF/GoP retrieval method and apparatus using the same
InactiveUS7519114B2Reduce the amount requiredReduce data volumeTelevision system detailsPicture reproducers using cathode ray tubesPattern recognitionGroup of pictures
Owner:SAMSUNG ELECTRONICS CO LTD
Identification or determination of a load based on texture
In one embodiment, the disclosure relates to a method for inspecting a load (101) in a container (100), comprising: classifying (S2) one or more patches (11) of a digitized inspection image (10), the digitized inspection image (10) being generated by an inspection system (1) configured to inspect the container (100) by transmission of inspection radiation (3) from an inspection radiation source (31) to an inspection radiation detector (32) through the container (100), wherein the classifying (S2) comprises: extracting (S21) one or more texture descriptors (V, P) of a patch (11), and classifying (S22) the patch (11), by comparing the one or more extracted texture descriptors (V, P) of the patch (11) to respective one or more reference texture descriptors (Vr, Wr, Pr) corresponding to respective one or more classes (202) of reference items (201), the one or more reference texture descriptors (Vr, Wr, Pr) of each class of reference items (201) being extracted from one or more reference images (20) of the one or more reference items (201).
Owner:SMITHS HEIMANN
Fast coding unit division method for H.266/VVC intra-frame prediction coding
InactiveCN114885172AReduce coding timeReduce computational complexityDigital video signal modificationBinary treeThresholding
According to the fast coding unit division method for H.266 / VVC intra-frame prediction coding, the texture complexity of a current coding CU is judged according to a texture descriptor, if the current coding CU is a flat region, division of the current CU is terminated, quadtree division is carried out on a CU block with the size of 64 * 64 to obtain a CU block with the size of 32 * 32, horizontal and vertical activity degrees are calculated, and the CU block with the size of 32 * 32 is obtained. If the difference between the horizontal activity degree and the vertical activity degree is smaller than a set threshold value, a VTM default mode is adopted, if the horizontal activity degree is larger than the vertical activity degree, texture activity degrees of quadtree division, horizontal binary tree division and horizontal ternary tree division are calculated, and if the vertical activity degree is larger than the horizontal activity degree, texture activity degrees of quadtree division, horizontal binary tree division and horizontal ternary tree division are calculated; the texture activity degrees of quadtree division, vertical binary tree division and vertical ternary tree division are calculated, the texture activity degrees are judged, a corresponding division mode is selected, and otherwise, a VTM default mode is adopted; according to the method, flat CU block division can be terminated in advance, a large amount of cost calculation is skipped, and the coding time is simply and effectively saved.
Owner:HUAQIAO UNIVERSITY
Method for image texture describing
A method for retrieving an image texture descriptor for describing texture features of an image, including the steps of (a) filtering input images using predetermined filters having different orientation coefficients, (b) projecting the filtered images onto axes of each predetermined direction to obtain data groups consisting of averages of each directional pixel values, (c) selecting candidate data groups among the data groups by a predetermined classification method, (d) determining a plurality of indicators based on orientation coefficients of the filters used in filtering the candidate data groups, and (e) determining the plurality of indicators as the texture descriptor of the image. The texture descriptors which allow kinds of texture structure present in an image to be perceptually captured can be retrieved.
Owner:SAMSUNG ELECTRONICS CO LTD
Digital video texture analytic method
Owner:SAMSUNG ELECTRONICS CO LTD +1
Digital image look-up method
A digital image texture analyzing method uses the mean and variance of the pixel values of an original image. Further, filtered images are obtained by filtering the original image using predetermined filters, each having a unique combination of one of m scales and one of n orientations, where m and n are predetermined positive integers, and the means and variances of the respective filtered images are calculate. A texture descriptor is obtained having the mean and variance of the pixel values of the original image and the means and variances of the respective filtered images obtained in the step, as texture features. The texture analyzing method allows image textures to be more accurately analyzed and compared for similarity, even when an image is rotated and / or enlarged or reduced relative to another image.
Owner:SAMSUNG ELECTRONICS CO LTD +1
One shot color calibrated metric learning for object re-identification
Techniques for detecting objects across images captured by camera devices. Embodiments capture, using first and second camera devices, first and second pluralities of images, respectively. First and second reference images are captured using the first and second camera devices. Color descriptors are extracted from the first plurality of images and the second plurality of images, and texture descriptors are extracted from the first plurality of images and the second plurality of images. Embodiments model a first color subspace and a second color subspace for the first camera device and the second camera device, respectively, based on the first and second pluralities of images and the first and second reference images. A data model for identifying objects appearing in images captured using the first and second camera devices is generated, based on the extracted color descriptors, texture descriptors and the first and second color subspaces.
Owner:DISNEY ENTERPRISES INC
Detection of nerves in a series of echographic images
The invention concerns a method for detecting a nerve in a series of echographic images comprising, for each image of said series: a step (E2) of generating a map of probabilities over regions of theimage, consisting of applying a plurality of texture descriptors in order to generate, for each pixel, a vector determining the response of the pixel for each of the descriptors, and then deducing a probability for each pixel of belonging to a nerve as a function of a probabilistic model; a classification step (E3) applied on the zones determined by this probability map, consisting of searching models corresponding to a nerve type, in a window sliding over these zones, assigning a degree of confidence to each position of the window and retaining an optimum position for each model; then analysing the consistency of these optimum positions by measuring their stability over a set of images of the series in order to select the window having the maximum consistency, which corresponds to a detected nerve.
Owner:UNIV OF ORLEANS +1
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