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Proceedings Paper

Wavelet-based higher-order neural networks for mine detection in thermal IR imagery
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Paper Abstract

An image processing technique is described for the detection of miens in RI imagery. The proposed technique is based on a third-order neural network, which processes the output of a wavelet packet transform. The technique is inherently invariant to changes in signature position, rotation and scaling. The well-known memory limitations that arise with higher-order neural networks are addressed by (1) the data compression capabilities of wavelet packets, (2) protections of the image data into a space of similar triangles, and (3) quantization of that 'triangle space'. Using these techniques, image chips of size 28 by 28, which would require 0(109) neural net weights, are processed by a network having 0(102) weights. ROC curves are presented for mine detection in real and simulated imagery.

Paper Details

Date Published: 22 August 2000
PDF: 11 pages
Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); doi: 10.1117/12.396244
Show Author Affiliations
Brian A. Baertlein, The Ohio State Univ. (United States)
Wen-Jiao Liao, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 4038:
Detection and Remediation Technologies for Mines and Minelike Targets V
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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