Segmentation of Multisensor Images using Evidential Combination in a Markovian Environment. Segmentation d'Images Multisenseur par Fusion Évidentielle dans un Contexte Markovien

Segmentation of Multisensor Images using Evidential Combination in a Markovian Environment

Segmentation d'Images Multisenseur par Fusion Évidentielle dans un Contexte Markovien

Azzeddine Bendjebbour Wojciech Pieczynski 

Laboratoire de Statistique ThéoriqueetAppliquée, Université Paris VI, 4, Place Jussieu, 75005 Paris, France

DépartementSignal et Image, Institut National des Télécommunications, 9, rue Charles Fourier, 91000 Evry, France

Page: 
453-464
|
Received: 
26 November 1996
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

The Dempster-Shafer combination rule turns out to be quite efficient in the segmentation of multisensor images in numerous situations. On the other hand, in a Bayesian framework the Hidden Markov Random Fields have been of interest for some twenty years. The aim of our work is to propose some methods capable of merging both evidential and Markovian field advantages. The interest of the methods proposed and the differences in their behaviour are studied through simulations on synthetic images. 

Résumé 

The Dempster-Shafer combination rule turns out to be quite efficient in the segmentation of multisensor images in numerous situations. On the other hand, in a Bayesian framework the Hidden Markov Random Fields have been of interest for some twenty years. The aim of our work is to propose some methods capable of merging both evidential and Markovian field advantages. The interest of the methods proposed and the differences in their behaviour are studied through simulations on synthetic images. 

Keywords: 

Theory of evidence, Hidden Markov fields, Dempster-Shafer combination rule, Image segmentation.

Mots clés 

Théoriedel'évidence, champs de Markov cachés,fusion de Dempster-Shafer,segmentationd'images.

1. Introduction
2. Segmentations Statistiques et Théorie de l'Évidence
3. Modèle d'Affaiblissement
4. Modèle Général
5. Conclusion
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