CLUSTER EXPANSIONS FOR THE DETERMINISTIC COMPUTATION OF BAYESIAN-ESTIMATORS BASED ON MARKOV RANDOM-FIELDS

被引：14

作者：

WU, CH ^{[1
]}

DOERSCHUK, PC ^{[1
]}

机构：

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

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1995年 / 17卷 / 03期

基金：

美国国家科学基金会;

关键词：

MARKOV RANDOM FIELDS; IMAGE RESTORATION; BAYESIAN; ESTIMATION; THRESHOLDED POSTERIOR MEAN ESTIMATOR;

D O I：

10.1109/34.368192

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We describe a family of approximations, denoted by ''cluster approximations,'' for the computation of the mean of a Markov random field (MRF). This is a key computation in image processing when applied to the a posteriori MRF. The approximation is to, account exactly for only spatially local interactions. Application of the approximation requires the solution of a nonlinear multivariable fixed-point equation for which we prove several existence, uniqueness, and convergence-of-algorithm results. Four numerical examples are presented, including comparison with Monte Carlo calculations.

引用

页码：275 / 293

页数：19

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