Markov Random Fields in Image Segmentation. Collection Foundation and Trends in Signal Processing

Abstract : Markov Random Fields in Image Segmentation provides an introduction to the fundamentals of Markovian modeling in image segmentation as well as a brief overview of recent advances in the field. Segmentation is formulated within an image labeling framework, where the prob- lem is reduced to assigning labels to pixels. In a probabilistic approach, label dependencies are modeled by Markov random fields (MRF) and an optimal labeling is determined by Bayes- ian estimation, in particular maximum a posteriori (MAP) estimation. The main advantage of MRF models is that prior information can be imposed locally through clique potentials. MRF models usually yield a non-convex energy function. The minimization of this function is crucial in order to find the most likely segmentation according to the MRF model. Classical optimiza- tion algorithms including simulated annealing and deterministic relaxation are treated along with more recent graph cut-based algorithms. The primary goal of this monograph is to demonstrate the basic steps to construct an eas- ily applicable MRF segmentation model and further develop its multi-scale and hierarchical implementations as well as their combination in a multilayer model. Representative exam- ples from remote sensing and biological imaging are analyzed in full detail to illustrate the applicability of these MRF models.
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Submitted on : Monday, October 1, 2012 - 11:11:19 AM
Last modification on : Saturday, January 27, 2018 - 1:31:39 AM

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Zoltan Kato, Josiane Zerubia. Markov Random Fields in Image Segmentation. Collection Foundation and Trends in Signal Processing. Now Editor, World Scientific, pp.164, 2012. ⟨hal-00737058⟩

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