Title :
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
fDate :
4/1/1995 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
