Title :
Texture classification using noncausal hidden Markov models
Author :
Povlow, Bennett R. ; Dunn, Stanley M.
Author_Institution :
Locheed Martin Astro Space, Princeton, NJ, USA
fDate :
10/1/1995 12:00:00 AM
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
Keywords :
computer vision; hidden Markov models; image classification; image texture; learning (artificial intelligence); computer vision; learning; neighbors; noncausal HMM; noncausal hidden Markov models; pixel; statistical method; texture classification; texture modeling; Classification algorithms; Computer vision; Hidden Markov models; Higher order statistics; Performance evaluation; Pixel; Probability distribution; Robustness; Statistical analysis; Testing;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Conference_Location :
10/1/1995 12:00:00 AM
