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• W130843938 abstract "The basic frameworks and practical schemes of the Bayesian network and the belief propagation to the probabilistic image processing are reviewed. The probabilistic image processing is formulated by means of Bayesian statistics and Markov random fields. The system is regarded as one of Bayesian networks. In general, the Bayesian network has serious computational complexity because the probabilistic models include a number of random variables for nodes or pixels. Recently, many researchers in the intermediate region of the mathematical sciences and the computer sciences are interested in the belief propagations, which is one of powerful approximate methods for probabilistic inference. In the present tutorial lecture note, we briefly explain the formulation of the probabilistic image processing and the theoretical structure of the belief propagation. As an examples of the applications of the probabilistic image processing, we review a noise reduction by means of Gaussian graphical model as Markov random fields." @default.
• W130843938 created "2016-06-24" @default.
• W130843938 creator A5067614812 @default.
• W130843938 date "2005-01-01" @default.
• W130843938 modified "2023-09-27" @default.
• W130843938 title "Probabilistic image processing and Bayesian network" @default.
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