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Proceedings Paper

Sea mine detection with a nearest-neighbor classifier based on residual vector quantization
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Paper Abstract

The feasibility of using residual (multiple stage) vector quantizer codevectors in a nearest neighbor classifier for direct classification of sonar pixel data is established. This approach combines the successive approximation process generated by the residual vector quantizer with successive decision making. Experimental results show that the probability of detection is about 80% and that the false alarm rate if about 5.6 false alarms per image. These initial performance benchmarks are encouraging considering the heuristic manner in which the residual vector quantizer codebooks were employed in the nearest neighbor classifier.

Paper Details

Date Published: 31 May 1996
PDF: 12 pages
Proc. SPIE 2765, Detection and Remediation Technologies for Mines and Minelike Targets, (31 May 1996); doi: 10.1117/12.241219
Show Author Affiliations
Christopher F. Barnes, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2765:
Detection and Remediation Technologies for Mines and Minelike Targets
Abinash C. Dubey; Robert L. Barnard; Colin J. Lowe; John E. McFee, Editor(s)

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