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Proceedings Paper

Mine detection in sonar images with minimal user input
Author(s): Jose Luis Lisani; Jean-Michel Morel; Lenny I. Rudin
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Paper Abstract

A parameter-free and a priori-information-free preprocessing of sonar images is proposed, which permits a ranking of local extrema in the image according to their likelihood to be amine-like objects. It is shown that an acceptable fully automatic detection algorithm can be built on a variational method which estimates shape information of the possible mines. This algorithm does not use any a priori information on the type of mine or range distance or background type and works without any change on both sonar databases we had available. It therefore can be used as a detection algorithm without any information request the use or designer. Its results could be fed into a classification algorithm like the one proposed. We also think that the features computed by this variational method could serve for both the detection step and the classification step, thus reducing the number of designer's parameters and opening the way to a parameter-free detection-classification algorithm.

Paper Details

Date Published: 2 August 1999
PDF: 12 pages
Proc. SPIE 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, (2 August 1999); doi: 10.1117/12.357081
Show Author Affiliations
Jose Luis Lisani, Cognitech, Inc. (United States)
Jean-Michel Morel, Cognitech, Inc. (France)
Lenny I. Rudin, Cognitech, Inc. (United States)

Published in SPIE Proceedings Vol. 3710:
Detection and Remediation Technologies for Mines and Minelike Targets IV
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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