27 April 2007 Confidence level fusion of edge histogram descriptor, hidden Markov model, spectral correlation feature, and NUKEv6
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Abstract
This paper examines the confidence level fusion of several promising algorithms for the vehicle-mounted ground penetrating radar landmine detection system. The detection algorithms considered here include Edge Histogram Descriptor (EHD), Hidden Markov Model (HMM), Spectral Correlation Feature (SCF) and NUKEv6. We first form a confidence vector by collecting the confidence values from the four individual detectors. The fused confidence is assigned to be the difference in the square of the Mahalanobis distance to the non-mine class and the square of the Mahalanobis distance to the mine class. Experimental results on a data collection that contains over 1500 mine encounters indicate that the proposed fusion technique can reduce the false alarm rate by a factor of two at 90% probability of detection when compared to the best individual detector.
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K. C. Ho, K. C. Ho, P. D. Gader, P. D. Gader, H. Frigui, H. Frigui, J. N. Wilson, J. N. Wilson, "Confidence level fusion of edge histogram descriptor, hidden Markov model, spectral correlation feature, and NUKEv6", Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 655320 (27 April 2007); doi: 10.1117/12.721005; https://doi.org/10.1117/12.721005
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