Unsupervised signal restoration using hidden Markov chains with copulas

被引：30
|
作者：
Brunel, N
Pieczynski, W
机构：
[1] GET, INT Dept CITI, CNRS, UMR 5157, F-91000 Evry, France
[2] Univ Paris 06, LSTA, F-75013 Paris, France
来源：
SIGNAL PROCESSING | 2005年 / 85卷 / 12期
关键词：
hidden Markov chains; pairwise Markov chains; triplet Markov chains; copulas; parameter estimation; Bayesian restoration; stochastic expectation-maximization; statistical image segmentation;
D O I：
10.1016/j.sigpro.2005.01.018
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 ; 0809 ;
摘要：
This paper deals with the statistical restoration of hidden discrete signals, extending the classical methodology based on hidden Markov chains. The aim is to take into account the hidden signal and complex relationships between the noises which can be from different parametric models, non-independent, and of class-varying nature. We discuss some possibilities of managing it using copulas. Further, we propose a parameter estimation method and apply resulting unsupervised restoration methods in variety of situations. It is also validated by experiments performed in supervised and unsupervised context. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码：2304 / 2315
页数：12
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