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Proceedings Paper

Markov random field model and fuzzy formalism-based data modeling for the sea-floor classification
Author(s): Max Mignotte; Christophe Collet; Patrick Perez; Patrick Bouthemy
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Paper Abstract

In this paper, we propose an original and statistical method for he sea-floor segmentation and its classification into five kinds of regions: sand, pebbles, rocks, ridges and dunes. The proposed method is based on the identification of the cast shadow shapes for each sea-bottom type and consists in four stages of processing. Firstly, the input image is segmented into two kinds of regions: shadow and sea-bottom reverberation. Secondly, the image of the contours of the detected cast shadows is partitioned into sub-windows from which a relevant geometrical feature vector is extracted. A pre-classification by a fuzzy classifier is thus required to initialize the third stage of processing. Finally, a Markov Random Field model is employed to specify homogeneity properties of the desired segmentation map. A Bayesian estimate of this map is computed using a deterministic relaxation algorithm. Reported experiments demonstrate that the proposed approach yields promising results to the problem of sea-floor classification.

Paper Details

Date Published: 25 June 1999
PDF: 12 pages
Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); doi: 10.1117/12.351317
Show Author Affiliations
Max Mignotte, Ecole Navale (Canada)
Christophe Collet, Ecole Navale (France)
Patrick Perez, IRISA/INRIA (France)
Patrick Bouthemy, IRISA/INRIA (France)


Published in SPIE Proceedings Vol. 3816:
Mathematical Modeling, Bayesian Estimation, and Inverse Problems
Françoise J. Prêteux; Ali Mohammad-Djafari; Edward R. Dougherty, Editor(s)

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