Estimation of generalized mixture in the case of correlated sensors

Author

Pieczynski, Wojciech ; Bouvrais, Julien ; Michel, Christophe

Author_Institution

Dept. Signal et Image, Inst. Nat. des Telecommun., Evry, France

Volume

9

Issue

2

fYear

2000

fDate

2/1/2000 12:00:00 AM

Firstpage

308

Lastpage

312

Abstract

This paper deals with unsupervised Bayesian classification of multidimensional data. We propose an extension of a previous method of generalized mixture estimation to the correlated sensors case. The method proposed is valid in the independent data case, as well as in the hidden Markov chain or field model case, with known applications in signal processing, particularly speech or image processing. The efficiency of the method proposed is shown via some simulations concerning hidden Markov fields, with application to unsupervised image segmentation

Keywords

Bayes methods; array signal processing; correlation methods; hidden Markov models; image classification; image segmentation; parameter estimation; unsupervised learning; correlated sensors; efficiency; field model; generalized mixture estimation; hidden Markov chain; hidden Markov fields; image processing; independent data; multidimensional data; multisensor data; signal processing; simulations; speech processing; unsupervised Bayesian classification; unsupervised image segmentation; Bayesian methods; Computer aided software engineering; Gaussian noise; Hidden Markov models; Image segmentation; Multidimensional signal processing; Multidimensional systems; Parameter estimation; Random processes; Speech processing;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.821750

Filename

821750