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Proceedings Paper

Statistical analysis of textures from compressed images
Author(s): Stephane Bonnevay; Michel P. Lamure; Nicolas Nicoloyannis
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Paper Abstract

This paper is devoted to a statistical analysis of textures from two codings. This analysis discriminates textures: a classification is built, not directly upon texture images, but upon a compressed information, which is created from each texture and composed of two coding images. The principle of this analysis is as follows: a texture images set is used. Each texture, composed of 256 gray levels, is encoded by two coding images of 15 colors. Some Kolmogorov-Smirnov's tests are carried out on combinations of two coding images in view to get a first discrimination. In the same time, from each coding image, co-occurrence parameters are computed. These parameters are used, with the previous discrimination, to get classifications. These classifications are compared to another one made only with co- occurrence parameters directly computed from a basis-textures set. In conclusion, we consider advantages and drawbacks of our approach and perspectives for the future.

Paper Details

Date Published: 16 April 1996
PDF: 10 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237903
Show Author Affiliations
Stephane Bonnevay, Univ. Claude Bernard Lyon I (France)
Michel P. Lamure, Univ. Claude Bernard Lyon I (France)
Nicolas Nicoloyannis, Univ. Claude Bernard Lyon I (France)

Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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