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Article Dans Une Revue IEEE Signal Processing Letters Année : 2012

Unsupervised segmentation of random discrete data hidden with switching noise distributions

Résumé

Hidden Markov models are very robust and have been widely used in a wide range of application fields; however, they can prove some limitations for data restoration under some complex situations. These latter include cases when the data to be recovered are nonstationary. The recent triplet Markov models have overcome such difficulty thanks to their rich formalism, that allows considering more complex data structures while keeping the computational complexity of the different algorithms linear to the data size. In this letter, we propose a new triplet Markov chain that allows the unsupervised restoration of random discrete data hidden with switching noise distributions. We also provide genuine parameters estimation and MPM restoration algorithms. The new model is validated through experiments conducted on synthetic data and on real images, whose results show its interest with respect to the standard hidden Markov chain.

Dates et versions

hal-00738142 , version 1 (03-10-2012)

Identifiants

Citer

Mohamed El Yazid Boudaren, Emmanuel Monfrini, Wojciech Pieczynski. Unsupervised segmentation of random discrete data hidden with switching noise distributions. IEEE Signal Processing Letters, 2012, 19 (10), pp.619-622. ⟨10.1109/LSP.2012.2209639⟩. ⟨hal-00738142⟩
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