Samsung Electronics Co., Ltd.

University of California

Other

Electronics and Telecommunications Research Institute

Applera Corporation

Curitel Communications, Inc.

Information and Communications University Educational Foundation

Heinrich-hertz-institut Fur Nachrichtentechnik Berlin Gesellschaft MIT Beschrankter Haftung

Hyun-doo Shin

Yang-lim Choi

Bangalore S Manjunath

Yining Deng

Peng Wu

Xinding Sun

Yong-man Ro

Yang-Iim Choi

Mun-churl Kim

Jin-woong Kim

A digital video processing method and an apparatus thereof are provided. The method for processing digital images received in the form of compressed video streams comprising the step of determining a region intensity histogram (RIH) based on information on motion compensation of inter frames. The RIH information is obtained based on the motion compensation values of inter frames, and the RIH information is a good indicator of motion information of a video scene. Also, since the RIH information is quite a good indicator of intensity of the video scene, video streams having similar intensities can be effectively searched by searching for similar video scenes based on the RIH information obtained by the digital video processing method.

Digital video processing method and apparatus thereof

A method for describing pattern repetitiveness which may exist within an image, is provided. The method of describing pattern repetitiveness of an image comprises: (a) projecting an image on a predetermined axis having a predetermined direction, (b) decomposing the projected image down one level, (c) increasing a threshold value while a pattern quantizing value is retained, and denoising the decomposed data, and (d) describing pattern repetitiveness of the image using the pattern quantizing value of the denoised data and the threshold value used for denoising. Because the method of describing pattern repetitiveness of an image effectively denoises the original image without damaging the pattern repetitiveness of the original image, and the pattern repetitiveness is described using the denoised data, it is possible to describe more definite pattern repetitiveness.

Method of describing pattern repetitiveness of image

A method of measuring a similarity between images is provided. The method includes the steps of (a) computing a statistical dissimilarity between the images; (b) computing a perceptual dissimilarity between the images; and (c) computing a dissimilarity between the texture features of the images based on the statistical dissimilarity and the perceptual dissimilarity. When images perceptually similar to a query image are searched and retrieved according to the above measuring method, the possibility that the retrieved images are not perceptually similar to the query image is small. Accordingly, the image searching performance can be improved when searching images having texture features similar to that of a query image.

Method and device for measuring similarity between images

A color image processing method is provided. The color image processing method includes the step of: (a) indexing a color image by assigning representative colors of an image to a color space divided into a plurality of regions. The color image processing method may be applied to object-based image processing such as MPEG-7, and fast search and retrieval of multimedia content can be made.

Color image processing method

Color image processing method for indexing an image using a lattice structure

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.

Method and apparatus for image texture describing

A digital image watermarking method is provided. The digital image watermarking method includes the steps of combining host image data sets and signature image data sets by a predetermined method for combining the sets to be linearly independent, and generating a watermarked image and a key from the combined image. A signature image embedded in a combined image is similar to the combined image in view of existence and durability.

Digital image watermarking method

Method for image texture describing

A color image segmentation method is provided. The color image segmentation method includes the steps of: (a) calculating a first value representing the degree of difference between a pixel and the color of peripheral pixels based a plurality of pixel values of an input image; (b) obtaining a converted image by converting the first calculated value into a value of a predetermined scale; and (c) segmenting the converted image. According to the color image segmentation method, an effective and an automatic segmentation is possible, and a segmentation speed is high even when segmenting an image containing much noise.

Color image segmentation method

A color image processing method and apparatus thereof are provided. The color image processing method includes the steps of (a) sorting image pixels according to the color distance between the image pixels and a central pixel, (b) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and (c) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups. The color image processing method can reduce the generation of edge blurring of the image in removing impulse noise from an image and filtering the same.

Color image processing method and apparatus thereof

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&#xd7;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&#xd7;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.

Texture description method and texture-based image retrieval method using Gabor filter in frequency domain

A color image processing method for retrieving a color feature descriptor for describing color features of an image is provided. The color image processing method includes the steps of (a) obtaining color vectors of an input image, (b) classifying the color vectors to obtain dominant colors of the input image and the ratios thereof, and (c) representing the dominant colors and the ratios thereof as a color feature descriptor of the input image. The color image processing method is applied to an object-based image processing method, thereby allowing fast search and retrieval of multi-media contents.

A digital image texture analyzing method including the steps of (a) obtaining the mean (&#x3bc;) and variance (&#x3c3;) of the pixel values of an original image, and (b) obtaining m&#xd7;n filtered images 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, (c) calculating the means (&#x3bc;, &#x3bc;, . . . , &#x3bc;) and variances (&#x3c3;, &#x3c3;, . . . , &#x3c3;) of the respective filtered images, and (d) obtaining a texture descriptor having the mean (&#x3bc;) and variance (&#x3c3;) of the pixel values of the original image obtained in the step (a), and the means (&#x3bc;, &#x3bc;, . . . , &#x3bc;) and variances (&#x3c3;, &#x3c3;, . . . , &#x3c3;) 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. Also, according to the digital image texture analyzing method, when an image is only rotated, enlarged or reduced relative to another image, the similarity of the textures of two images are analyzed, that is, accurate analysis can be performed.

Growing community of inventors

Sungnam, South Korea

Hyun-doo Shin