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Signal and Image Segmentation Using Pairwise Markov Chains (2003)
| Content Provider | CiteSeerX |
|---|---|
| Author | Derrode, Stéphane Pieczynski, Wojciech Derrode, S. Pieczynski, W. |
| 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, 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 classical hidden Markov chain one for supervised and unsupervised restorations. |
| File Format | |
| Publisher Date | 2003-01-01 |
| Access Restriction | Open |
| Subject Keyword | Unsupervised Restoration Mixture Context Classical Hidden Markov Chain Hidden Data Original Parameter Estimation Method Main Novelty General Setting Several Experimental Result Hidden Markov Chain Pairwise Markov Chain Model Recent Pairwise Markov Chain Model |
| Content Type | Text |
| Resource Type | Article |
