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Double Markov random fields and Bayesian image segmentation
2002
IEEE Transactions on Signal Processing
Markov random fields are used extensively in modelbased approaches to image segmentation and, under the Bayesian paradigm, are implemented through Markov chain Monte Carlo (MCMC) methods. In this paper, we describe a class of such models (the double Markov random field) for images composed of several textures, which we consider to be the natural hierarchical model for such a task. We show how several of the Bayesian approaches in the literature can be viewed as modifications of this model, made
doi:10.1109/78.978390
fatcat:nj5ey6h7gbe6tedywljnhuvc74