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Probabilistic Image Processing based on the Q-Ising Model by Means of the Mean-Field Method and Loopy Belief Propagation
Cambridge UK August 23-August 26
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.133401217th International Conference on Patt ...
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Kazuyuki Tanaka, Tohoku University, Japan
D. M. Titterington, University of Glasgow, UK
The framework is presented of Bayesian image restoration for multi-valued images by means of the Q-Ising model. Hyperparameters in the probabilistic model are determined so as to maximize the marginal likelihood. Practical algorithms are described based the conventional mean-field approximation and loopy belief propagation. We compare the results empirically with those provided by conventional filters and the new methodsarefound to be superior.
Citation:
Kazuyuki Tanaka, D. M. Titterington, "Probabilistic Image Processing based on the Q-Ising Model by Means of the Mean-Field Method and Loopy Belief Propagation," icpr, vol. 2, pp.40-43, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 2, 2004
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