Feature learning for a hidden Markov model approach to landmine detection

Xuping Zhang; Paul Gader; Hichem Frigui

doi:10.1117/12.722593

27 April 2007 Feature learning for a hidden Markov model approach to landmine detection

Xuping Zhang, Paul Gader, Hichem Frigui

Proceedings Volume 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII; 655327 (2007) https://doi.org/10.1117/12.722593
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

Xuping Zhang, Paul Gader, and Hichem Frigui "Feature learning for a hidden Markov model approach to landmine detection", Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 655327 (27 April 2007); https://doi.org/10.1117/12.722593

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