Signal and image segmentation using pairwise Markov chains

被引：79

作者：

Derrode, S

Pieczynski, W

机构：

[1] Ecole Natl Super Phys Marseille, Inst Fresnel, GSM Grp, CNRS,UMR 6133, F-13013 Marseille 20, France

[2] Inst Natl Telecommun, CNRS, UMR 5157, Dept CITI,GET INT, F-91000 Evry, France

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2004年 / 52卷 / 09期

关键词：

Bayesian restoration; hidden data; hidden Markov chain; image segmentation; iterative conditional estimation; maximal posterior mode (MPM); maximum a posteriori; pairwise Markov chain; Pearson' system; unsupervised classification;

D O I：

10.1109/TSP.2004.832015

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 [电气工程]; 0809 [电子科学与技术];

摘要：

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.

引用

页码：2477 / 2489

页数：13

共 50 条

[1]

Applications of hidden Markov chains in image analysis [J].