Tree approximations to Markov random fields

Author

Wu, Chi-hsin ; Doerschuk, Peter C.

Author_Institution

Dept. of Image Process., Opto-Electron. & Syst. Lab., Hsinchu, Taiwan

Volume

17

Issue

4

fYear

1995

fDate

4/1/1995 12:00:00 AM

Firstpage

391

Lastpage

402

Abstract

Methods for approximately computing the marginal probability mass functions and means of a Markov random field (MRF) by approximating the lattice by a tree are described. Applied to the a posteriori MRF these methods solve Bayesian spatial pattern classification and image restoration problems. The methods are described, several theoretical results concerning fixed-point problems are proven, and four numerical examples are presented, including comparison with optimal estimators and the iterated conditional mode estimator and including two agricultural optical remote sensing problems

Keywords

Bayes methods; Markov processes; image restoration; image segmentation; pattern classification; probability; remote sensing; trees (mathematics); Bayesian spatial pattern classification; Markov random fields; agricultural optical remote sensing problems; fixed-point problems; image restoration; iterated conditional mode estimator; marginal probability mass functions; optimal estimators; tree approximations; Bayesian methods; Cultural differences; Image restoration; Image segmentation; Lattices; Markov random fields; Optical sensors; Pattern classification; Remote sensing; Tree graphs;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.385979

Filename

385979