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- W2024684433 abstract "Ground-penetrating radar (GPR) is a very useful technology for buried threat detection applications which iscapable of identifying both metallic and non-metallic objects with moderate false alarm rates. Several patternclassication algorithms have been proposed and evaluated which enable GPR systems to achieve robust per-formance. However, comparisons of these algorithms have shown that their relative performance varies withrespect to the environmental context under which the GPR is operating. Context-dependent fusion has beenproposed as a technique for algorithm fusion and has been shown to improve performance by exploiting thedierences in algorithm performance under dierent environmental and operating conditions. Early approachesto context-dependent fusion clustered observations in the joint condence space of all algorithms and appliedfusion rules within each cluster (i.e., discriminative learning). Later approaches exploited physics-based fea-tures extracted from the background data to leverage more environmental information, but decoupled contextlearning from algorithm fusion (i.e., generative learning). In this work, a Bayesian inference technique whichcombines the generative and discriminative approaches is proposed for physics-based context-dependent fusionof detection algorithms for GPR. The method uses a Dirichlet process (DP) mixture as a model for context, andrelevance vector machines (RVMs) as models for algorithm fusion. Variational Bayes is used as an approximateinference technique for joint learning of the context and fusion models. Experimental results compare the pro-posed Bayesian discriminative technique to generative techniques developed in past work by investigating thesimilarities and dierences in the contexts learned as well as overall detection performance." @default.
- W2024684433 created "2016-06-24" @default.
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- W2024684433 date "2012-05-10" @default.
- W2024684433 modified "2023-09-23" @default.
- W2024684433 title "A Bayesian method for discriminative context-dependent fusion of GPR-based detection algorithms" @default.
- W2024684433 doi "https://doi.org/10.1117/12.919079" @default.
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