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Decorrelation Methods of Texture Feature Extraction

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2 Author(s)
Olivier D. Faugeras ; MEMBER, IEEE, Institut de Recherche d'Informatique et d'Automatique, Domaine de Voluceau, Rocquencourt, Le Chesnay, France; Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90007. ; William K. Pratt

This paper presents the development and evaluation of a visual texture feature extraction method based on a stochastic field model of texture. Results of recent visual texture discrimination experiments are reviewed in order to establish necessary and sufficient conditions for texture features that are in agreement with human discrimination. A texture feature extraction technique involving autocorrelation function measurement of a texture field, combined with histogram representation of a statistically decorrelated version of the texture field, is introduced. The texture feature extraction method is evaluated in terms of a Bhattacharyya distance measure.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-2 ,  Issue: 4 )