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Unsupervised segmentation of non stationary data hidden with non stationary noise

Abstract : Classical hidden Markov chains (HMC) can be inefficient in the unsupervised segmentation of non stationary data. To overcome such involvedness, the more elaborated triplet Markov chains (TMC) resort to using an auxiliary underlying process to model the behavior switches within the hidden states process. However, so far, only this latter was considered non stationary. The aim of this paper is to extend the results of a recently proposed TMC by considering both hidden states and noise non stationary. To show the efficiency of the proposed model, we provide results of non stationary synthetic and real images restoration
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https://hal.archives-ouvertes.fr/hal-01354693
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Friday, August 19, 2016 - 11:27:22 AM
Last modification on : Saturday, September 26, 2020 - 3:25:42 AM

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Mohamed El Yazid Boudaren, Wojciech Pieczynski, Emmanuel Monfrini. Unsupervised segmentation of non stationary data hidden with non stationary noise. WoSSPA 2011 : 7th International Workshop on Systems, Signal Processing and their Applications, May 2011, Tipaza, Algeria. pp.255 - 258, ⟨10.1109/WOSSPA.2011.5931466⟩. ⟨hal-01354693⟩

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