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Proceedings Paper

Application of the Gibbs-Bogoliubov-Feynman inequality in mean field calculations for Markov random fields
Author(s): Jun Zhang
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Paper Abstract

Recently, there has been growing interest in the use of mean field theory (MFT) in Markov random field (MRF) model-based estimation problems. In many image processing and computer vision applications, the MFT approach can provide comparable performance to that of the simulated annealing, but requires much less computational effort and has easy hardware implementation. The Gibbs-Bogoliubov-Feynman inequality from statistical mechanics provides a general, systematic, and optimal approach for deriving mean field approximations. In this paper, this approach is applied to two important classes of MRF's, the compound Gauss-Markov model and the vector Ising model. The results obtained are compared and related to other methods of deriving mean field equations. Experimental results are also provided to demonstrate the efficacy of this approach.

Paper Details

Date Published: 16 September 1994
PDF: 12 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.186042
Show Author Affiliations
Jun Zhang, Univ. of Wisconsin/Milwaukee (United States)


Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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