Signal and image segmentation using pairwise Markov chains

Stéphane Derrode; Wojciech Pieczynski

doi:10.1109/TSP.2004.832015

Signal and image segmentation using pairwise Markov chains

Stéphane Derrode
, Wojciech Pieczynski

Institut Fresnel

Research output: Contribution to journal › Article › peer-review

Abstract

The aim of this paper is to apply the recent pairwise Markov chain model, which generalizes the hidden Markov chain one, to the unsupervised restoration of hidden data. The main novelty is an original parameter estimation method that is valid in a general setting, where the form of the possibly correlated noise is not known. Several experimental results are presented in both Gaussian and generalized mixture contexts. They show the advantages of the pairwise Markov chain model with respect to the classical hidden Markov chain one for supervised and unsupervised restorations.

Original language	English
Pages (from-to)	2477-2489
Number of pages	13
Journal	IEEE Transactions on Signal Processing
Volume	52
Issue number	9
DOIs	https://doi.org/10.1109/TSP.2004.832015
Publication status	Published - 1 Sept 2004

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title = "Signal and image segmentation using pairwise Markov chains",

abstract = "The aim of this paper is to apply the recent pairwise Markov chain model, which generalizes the hidden Markov chain one, to the unsupervised restoration of hidden data. The main novelty is an original parameter estimation method that is valid in a general setting, where the form of the possibly correlated noise is not known. Several experimental results are presented in both Gaussian and generalized mixture contexts. They show the advantages of the pairwise Markov chain model with respect to the classical hidden Markov chain one for supervised and unsupervised restorations.",

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AB - The aim of this paper is to apply the recent pairwise Markov chain model, which generalizes the hidden Markov chain one, to the unsupervised restoration of hidden data. The main novelty is an original parameter estimation method that is valid in a general setting, where the form of the possibly correlated noise is not known. Several experimental results are presented in both Gaussian and generalized mixture contexts. They show the advantages of the pairwise Markov chain model with respect to the classical hidden Markov chain one for supervised and unsupervised restorations.

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Signal and image segmentation using pairwise Markov chains

Abstract

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