DocumentCode
3558588
Title
Texture classification using noncausal hidden Markov models
Author
Povlow, Bennett R. ; Dunn, Stanley M.
Author_Institution
Locheed Martin Astro Space, Princeton, NJ, USA
Volume
17
Issue
10
fYear
1995
fDate
10/1/1995 12:00:00 AM
Firstpage
1010
Lastpage
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
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;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
Conference_Location
10/1/1995 12:00:00 AM
ISSN
0162-8828
Type
jour
DOI
10.1109/34.464564
Filename
464564
Link To Document