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Proceedings Paper

Performance comparison of neural networks for undersea mine detection
Author(s): Scott T. Toborg; Matthew Lussier; David Rowe
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Paper Abstract

This paper describes the design of an undersea mine detection system and compares the performance of various neural network models for classification of features extracted from side-scan sonar images. Techniques for region of interest and statistical feature extraction are described. Subsequent feature analysis verifies the need for neural network processing. Several different neural and conventional pattern classifiers are compared including: k-Nearest Neighbors, Backprop, Quickprop, and LVQ. Results using the Naval Image Database from Coastal Systems Station (Panama City, FL) indicate neural networks have consistently superior performance over conventional classifiers. Concepts for further performance improvements are also discussed including: alternative image preprocessing and classifier fusion.

Paper Details

Date Published: 2 March 1994
PDF: 12 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169967
Show Author Affiliations
Scott T. Toborg, Hughes Research Labs. (United States)
Matthew Lussier, Hughes Aircraft Surface Systems Div. (United States)
David Rowe, Hughes Aircraft Surface Systems Div. (United States)

Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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