Statistical analysis of textures from compressed images

Stephane Bonnevay; Michel P. Lamure; Nicolas Nicoloyannis

doi:10.1117/12.237903

16 April 1996 Statistical analysis of textures from compressed images

Stephane Bonnevay, Michel P. Lamure, Nicolas Nicoloyannis

Proceedings Volume 2710, Medical Imaging 1996: Image Processing; (1996) https://doi.org/10.1117/12.237903
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

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.

Citation Download Citation

Stephane Bonnevay, Michel P. Lamure, and Nicolas Nicoloyannis "Statistical analysis of textures from compressed images", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237903

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