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We propose a landmine detection algorithm using ground penetrating radar (GPR) data that uses multiple features and an ensemble of continuous hidden Markov models (CHMMs). Our approach is motivated by the need for different features and multiple models to capture the large variations of mine and clutter types and the different environmental conditions. First, for each feature representation, we fit...
The Edge Histogram Detector (EHD) is a well-researched and tested algorithm that has been integrated into fielded systems for landmine detection using Ground Penetrating Radar (GPR) sensor. It uses edge histogram based features and a possibilistic K-Nearest Neighbors (KNN) classifier. Due to the inherent static data representation and static classifier architecture, the EHD may not be very effective...
We propose a landmine detection algorithm using ground penetrating radar data that is based on an SVM classifier. The kernel function for the SVM is constructed using discrete hidden Markov modeling (HMM). Typically, the kernel matrix could be obtained by defining an adequate similarity measure in the feature space. However, this approach is inappropriate as it is not trivial to define a meaningful...
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