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3-D underwater object recognition

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2 Author(s)
D. Boulinguez ; Signal & Syst. Dept., Inst. Superieur d'Electron. du Nord, Lille, France ; A. Quinquis

In this paper, we propose an automatic supervised classification of objects lying on the sea floor or buried in sediment layers. This pattern recognition provides a way to distinguish natural and manufactured objects and then should be helpful to detect mine, pipe-line, or wreckage. Proposed methods combine different techniques: pattern information extraction, relevant parameter search, and supervised classifier. Parameters are automatically selected using a principal component analysis to reduce misclassification rate and to simplify classifier structure. Performances of different parameters (two-dimensional and three-dimensional) are compared and discussed from testing on synthetic and real data bases.

Published in:

IEEE Journal of Oceanic Engineering  (Volume:27 ,  Issue: 4 )