Side-attack mine detection via morphological image analysis

John McElroy; Chris Hawkins; Paul D. Gader; James M. Keller; Robert Luke

doi:10.1117/12.544326

21 September 2004 Side-attack mine detection via morphological image analysis

John McElroy, Chris Hawkins, Paul D. Gader, James M. Keller, Robert Luke

Proceedings Volume 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX; (2004) https://doi.org/10.1117/12.544326
Event: Defense and Security, 2004, Orlando, Florida, United States

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.

Citation Download Citation

John McElroy, Chris Hawkins, Paul D. Gader, James M. Keller, and Robert Luke "Side-attack mine detection via morphological image analysis", Proc. SPIE 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX, (21 September 2004); https://doi.org/10.1117/12.544326

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