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Modèles de Markov en traitement d'images

Abstract : The aim of this paper is to present some aspects of Markov model based statistical image processing. After a brief review of statistical processing in image segmentation, classical Markov models (fields, chains, and trees) used in image processing are developed. Bayesian methods of segmentation are then described and different general parameter estimation methods are presented. More recent models and processing techniques, such as Pairwise and Triplet Markov models, Dempster-Shafer fusion in a Markov context, and generalized mixture estimation, are then discussed. We conclude with a nonexhaustive desciption of candidate extensions to multidimensional, multisensor, and multiresolution imagery. Connections with general graphical models are also highlighted.
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Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Tuesday, July 19, 2016 - 11:50:42 AM
Last modification on : Wednesday, November 25, 2020 - 3:26:57 AM


  • HAL Id : hal-01346587, version 1


Wojciech Pieczynski. Modèles de Markov en traitement d'images. Traitement du Signal, Lavoisier, 2003, 20 (3-NS), pp.255 - 277. ⟨hal-01346587⟩



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