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Proceedings Paper

Enhancing mine signatures in sonar images using nested neural networks
Author(s): Jeffrey Paul Sutton; David D. Sha; Stuart W. Perry; Ling Guan
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Paper Abstract

An adaptive image regularization algorithm, based on the NoN neural computing theory, is applied to enhance mine signatures. The algorithm, developed by Guan and Sutton (GS), uses vector connections among model neurons to delineate dynamic boundaries corresponding to critical features of images. The boundaries subdivide large networks into many smaller networks, where each smaller network has, in many instances, attractor properties. In this report, the GS algorithm is applied to deblur and segment three sets of underwater mine data. The results suggest that the GS algorithm requires minimal training, performs well under inhomogeneous conditions and generates contours, which can be fed into other NoN architectures for further processing, including classification.

Paper Details

Date Published: 2 August 1999
PDF: 8 pages
Proc. SPIE 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, (2 August 1999); doi: 10.1117/12.357079
Show Author Affiliations
Jeffrey Paul Sutton, MGH Neural Systems Group and Harvard Univ. (United States)
David D. Sha, MGH Neural Systems Group, Harvard Univ., and Massachusetts Institute of Technology (United States)
Stuart W. Perry, Univ. of Sydney (Australia)
Ling Guan, Univ. of Sydney (Canada)

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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