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Texture Classification Using Noncausal Hidden Markov Models
October 1995 (vol. 17 no. 10)
pp. 1010-1014

Abstract—This paper addresses the problem of using noncausal hidden Markov models (HMMs) for texture classification. In noncausal models, the state of each pixel may be dependent on its neighbors in all directions. New algorithms are given to learn the parameters of a noncausal HMM of a texture and to classify it into one of several learned categories. Texture classification results using these algorithms are provided.

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Index Terms:
Texture, texture classification, texture modeling, hidden Markov model, noncausal hidden Markov model, computer vision, learning.
Citation:
Bennett R. Povlow, Stanley M. Dunn, "Texture Classification Using Noncausal Hidden Markov Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 1010-1014, Oct. 1995, doi:10.1109/34.464564
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