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Proceedings Paper

Feature learning for a hidden Markov model approach to landmine detection
Author(s): Xuping Zhang; Paul Gader; Hichem Frigui
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Paper Abstract

Hidden Markov Models (HMMs) are useful tools for landmine detection and discrimination using Ground Penetrating Radar (GPR). The performance of HMMs, as well as other feature-based methods, depends not only on the design of the classifier but on the features. Traditionally, algorithms for learning the parameters of classifiers have been intensely investigated while algorithms for learning parameters of the feature extraction process have been much less intensely investigated. In this paper, we describe experiments for learning feature extraction and classification parameters simultaneously in the context of using hidden Markov models for landmine detection.

Paper Details

Date Published: 27 April 2007
PDF: 12 pages
Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 655327 (27 April 2007); doi: 10.1117/12.722593
Show Author Affiliations
Xuping Zhang, Univ. of Florida (United States)
Paul Gader, Univ. of Florida (United States)
Hichem Frigui, Univ. of Louisville (United States)

Published in SPIE Proceedings Vol. 6553:
Detection and Remediation Technologies for Mines and Minelike Targets XII
Russell S. Harmon; J. Thomas Broach; John H. Holloway Jr., Editor(s)

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