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Issue No. 04 - April (1995 vol. 17)
ISSN: 0162-8828
pp: 391-402
ABSTRACT
<p><it>Abstract</it>—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.</p>
INDEX TERMS
Markov random field, Bayesian estimation, spatial pattern classification, image segmentation, image restoration.
CITATION

C. Wu and P. C. Doerschuk, "Tree Approximations to Markov Random Fields," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 17, no. , pp. 391-402, 1995.
doi:10.1109/34.385979
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