TREE APPROXIMATIONS TO MARKOV RANDOM-FIELDS

被引：20

作者：

WU, CH ^{[1
]}

DOERSCHUK, PC ^{[1
]}

机构：

[1] PURDUE UNIV,SCH ELECT ENGN,W LAFAYETTE,IN 47907

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1995年 / 17卷 / 04期

基金：

美国国家科学基金会;

关键词：

MARKOV RANDOM FIELD; BAYESIAN ESTIMATION; SPATIAL PATTERN CLASSIFICATION; IMAGE SEGMENTATION; IMAGE RESTORATION;

D O I：

10.1109/34.385979

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 MRP 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.

引用

页码：391 / 402

页数：12

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