36 results about "Texture Descriptor" patented technology

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.

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.

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).

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.

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.

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' or GoPs', 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' or GoPs' corresponding to the searched similar homogeneous texture descriptors and arranging GoFs' or GoPs' 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.

A digital image texture analyzing method uses (302) the mean ( mu 0) and variance ( sigma 0) of the pixel values of an original image. Further, m x n filtered images are obtained (304) 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 ( mu 11, mu 21, ..., mu mn) and variances ( sigma 11, sigma 21,..., sigma mn) of the respective filtered images are calculate (306). A texture descriptor is obtained (308) having the mean ( mu 0) and variance ( sigma 0) of the pixel values of the original image and the means ( mu 11, mu 21, ..., mu mn) and variances ( sigma 11, sigma 21,..., sigma mn) of the respective filtered images obtained in the step (c), 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.

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.

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.