Markov Random Field Model and Fuzzy

In this paper we propose an original and statistical method for the sea-oor segmentation and its classi-cation into ve kinds of regions: sand, pebbles, rocks, ridges and dunes. The proposed method is based on the identiication 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 (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) 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-classiication by a fuzzy classiier is thus required to initialize the third stage of processing. Finally, a Markov Random Field (MRF) 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-oor classiication.