25 June 1999 Markov random field model and fuzzy formalism-based data modeling for the sea-floor classification
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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.
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Max Mignotte, Christophe Collet, Patrick Perez, Patrick Bouthemy, "Markov random field model and fuzzy formalism-based data modeling for the sea-floor classification", Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); doi: 10.1117/12.351317; https://doi.org/10.1117/12.351317
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