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Proceedings Paper

Side-attack mine detection via morphological image analysis
Author(s): John McElroy; Chris Hawkins; Paul D. Gader; James M. Keller; Robert Luke
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Paper Abstract

Mathematical morphology is a field of knowledge and techniques involving the application of nonlinear image processing operations to perform image enhancement, feature extraction, and segmentation as well as a variety of other tasks. Morphological operations have previously been combined with neural networks to produce detectors that learn features and classification rules simultaneously. The previous networks have been demonstrated to provide the capability for detecting occluded vehicles of specific types using LADAR, SAR, Infrared, and Visible imagery. In this paper, we describe the application of morphological shared weight neural networks to detecting off-route, or “side attack”, mines. A pair of image sequences, both of the same scene, with and without a mine are presented to the system. The network then performs detection and decision-making on a per sequence basis.

Paper Details

Date Published: 21 September 2004
PDF: 8 pages
Proc. SPIE 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX, (21 September 2004); doi: 10.1117/12.544326
Show Author Affiliations
John McElroy, Univ. of Florida (United States)
Chris Hawkins, Univ. of Florida (United States)
Paul D. Gader, Univ. of Florida (United States)
James M. Keller, Univ. of Missouri/Columbia (United States)
Robert Luke, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 5415:
Detection and Remediation Technologies for Mines and Minelike Targets IX
Russell S. Harmon; J. Thomas Broach; John H. Holloway, Editor(s)

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