Information fusion in a Markov random field-based image segmentation approach using adaptive neighbourhoods

Author

Smits, P.C. ; Dellepiane, S.

Author_Institution

Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy

Volume

2

fYear

1996

fDate

25-29 Aug 1996

Firstpage

570

Abstract

In this paper an image segmentation method is proposed that is a modification to the Markov random field (MRF) region label process used by Rignot and Chellappa (1992). Using Bayesian inference, the optimal shape of the neighbourhood system is determined on the basis of the Markovian property. This MRF segmentation approach with adaptive neighbourhood systems (MRF-AN) makes it possible to better preserve small features by the combination of evidence from different knowledge sources. The purpose of the article is to show the validity of the concept of MRF-AN for image segmentation. Results are shown using synthetic aperture radar data

Keywords

Bayes methods; Markov processes; image segmentation; sensor fusion; Bayesian inference; Markov random field-based image segmentation; adaptive neighbourhoods; information fusion; optimal shape; region label process; synthetic aperture radar data; Adaptive systems; Bayesian methods; Costs; Image segmentation; Iterative methods; Markov random fields; Shape; Speckle; Synthetic aperture radar; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1996., Proceedings of the 13th International Conference on

Conference_Location

Vienna

ISSN

1051-4651

Print_ISBN

0-8186-7282-X

Type

conf

DOI

10.1109/ICPR.1996.546888

Filename

546888