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Communication Dans Un Congrès Année : 2016

Evidential correlated Gaussian mixture Markov model for pixel labeling problem

Résumé

Hidden Markov Fields (HMF) have been widely used in various problems of image processing. In such models, the hidden process of interest is assumed to be a Markov field that must be estimated from an observable process . Classic HMFs have been recently extended to a very general model called "evidential pairwise Markov field" (EPMF). Extending its recent particular case able to deal with non-Gaussian noise, we propose an original variant able to deal with non-Gaussian and correlated noise. Experiments conducted on simulated and real data show the interest of the new approach in an unsupervised context
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Dates et versions

hal-01369520 , version 1 (21-09-2016)

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Lin An, Ming Li, Mohamed El Yazid Boudaren, Wojciech Pieczynski. Evidential correlated Gaussian mixture Markov model for pixel labeling problem. BELIEF 2016: 4th International Conference on Belief Functions, Sep 2016, Prague, Czech Republic. pp.203 - 211, ⟨10.1007/978-3-319-45559-4_21⟩. ⟨hal-01369520⟩
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