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Proceedings Paper

Landmine detection using ensemble discrete hidden Markov models with context dependent training methods
Author(s): Anis Hamdi; Oualid Missaoui; Hichem Frigui; Paul Gader
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Paper Abstract

We propose a landmine detection algorithm that uses ensemble discrete hidden Markov models with context dependent training schemes. We hypothesize that the data are generated by K models. These different models reflect the fact that mines and clutter objects have different characteristics depending on the mine type, soil and weather conditions, and burial depth. Model identification is based on clustering in the log-likelihood space. First, one HMM is fit to each of the N individual sequence. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N x N log-likelihood distance matrix that will be partitioned into K groups. In the second step, we learn the parameters of one discrete HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we will investigate the maximum likelihood, and the MCE-based discriminative training approaches. Results on large and diverse Ground Penetrating Radar data collections show that the proposed method can identify meaningful and coherent HMM models that describe different properties of the data. Each HMM models a group of alarm signatures that share common attributes such as clutter, mine type, and burial depth. Our initial experiments have also indicated that the proposed mixture model outperform the baseline HMM that uses one model for the mine and one model for the background.

Paper Details

Date Published: 29 April 2010
PDF: 9 pages
Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76642J (29 April 2010); doi: 10.1117/12.852256
Show Author Affiliations
Anis Hamdi, Univ. of Louisville (United States)
Oualid Missaoui, Univ. of Louisville (United States)
Hichem Frigui, Univ. of Louisville (United States)
Paul Gader, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 7664:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV
Russell S. Harmon; John H. Holloway Jr.; J. Thomas Broach, Editor(s)

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