A model-based method for rotation invariant texture classification

Authors:
Rangasami L. Kashyap;Alireza Khotanzad
Affiliations:
Purdue Univ., West Lafayette, IN;Southern Methodist Univ., Dallas, TX
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1986

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Abstract

This paper presents a new model-based approach for texture classification which is rotation invariant, i.e., the recognition accuracy is not affected if the orientation of the test texture is different from the orientation of the training samples. The method uses three statistical features, two of which are obtained from a new parametric model of the image called a ``circular symmetric autoregressive model.'' Two of the proposed features have physical interpretation in terms of the roughness and directionality of the texture. The results of several classification experiments on differently oriented samples of natural textures including both microtextures and macrotextures are presented.