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Proceedings Paper

Detection of mine and minelike objects in forward-looking sonar data with direct sum successive approximation templates
Author(s): Christopher F. Barnes; Philip A. Hallenborg; Snehal Patel; Dave Fisher
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Paper Abstract

Nearest neighbor classifiers with direct sum successive approximation (DSSA) templates are shown to be effective for detecting and discriminating mines and mine-like objects in forward looking sonar data. DSSA results are demonstrated on data obtained form field measurements with actual mines and calibration targets. The DSSA templates are used in a nearest neighbor classifier that can be characterized as a new type of radial basis function neural network. This neural network is not designed with a preset complexity level as quantified by an a priori determined number of degrees-of-freedom. Rather, the system is constructed incrementally and adds additional degrees-of-freedom as required by the nature of the training data. The neural net system possesses stage structure that result in inherent computational and memory efficiency in searching and storing the DSSA-based radial basis functions.

Paper Details

Date Published: 2 August 1999
PDF: 5 pages
Proc. SPIE 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, (2 August 1999); doi: 10.1117/12.357088
Show Author Affiliations
Christopher F. Barnes, Georgia Tech Research Institute (United States)
Philip A. Hallenborg, Boeing Information, Space & Defense Systems (United States)
Snehal Patel, Boeing Information, Space & Defense Systems (United States)
Dave Fisher, Boeing Information, Space & Defense Systems (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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