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2 August 1999 Enhancing mine signatures in sonar images using nested neural networks
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeffrey Paul Sutton, David D. Sha, Stuart W. Perry, and Ling Guan "Enhancing mine signatures in sonar images using nested neural networks", Proc. SPIE 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, (2 August 1999); https://doi.org/10.1117/12.357079
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