The Community for Technology Leaders
Green Image
Issue No. 04 - April (1986 vol. 8)
ISSN: 0162-8828
pp: 472-481
Rangasami L. Kashyap , School of Electrical Engineering, Purdue University, West Lafayette, IN 47907.
Alireza Khotanzad , Department of Electrical Engineering, Southern Methodist University, Dallas, TX 75275.
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.

R. L. Kashyap and A. Khotanzad, "A Model-Based Method for Rotation Invariant Texture Classification," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 8, no. , pp. 472-481, 1986.
81 ms
(Ver 3.3 (11022016))