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Conference Papers Year : 2010

Hidden fuzzy Markov chain model with K discrete classes


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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hal-00616372 , version 1 (22-08-2011)


  • HAL Id : hal-00616372 , version 1


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. ⟨hal-00616372⟩
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