Hidden fuzzy Markov chain model with K discrete classes

Abstract : This paper deals with a new unsupervised fuzzy Bayesian segmentation method based on the hidden Markov chain model, in order to separate continuous from discrete components in the hidden data. We present a new F-HMC (fuzzy hidden Markov chain) related to three hard classes, based on a general extension of the previously algorithms proposed. For a given observation, the hidden variable owns a density according to a measure containing Dirac and Lebesgue components. We have performed our approach in the multispectral context. The hyper-parameters are estimated using a Stochastic Expectation Maximization (SEM) algorithm. We present synthetic simulations and also segmentation results related to real multi-band data.
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Communication dans un congrès
Information Sciences Signal Processing and their Applications (ISSPA), May 2010, Kuala Lumpur, Malaysia. 2010
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Contributeur : Ahmed Gamal Eldin <>
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Dernière modification le : lundi 22 août 2011 - 15:55:29
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  • HAL Id : hal-00616372, version 1

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Ahmed Gamal Eldin, Fabien Salzenstein, Christophe Collet. Hidden fuzzy Markov chain model with K discrete classes. Information Sciences Signal Processing and their Applications (ISSPA), May 2010, Kuala Lumpur, Malaysia. 2010. <hal-00616372>

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