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This paper proposes a probabilistic robust hyperbola mixture model based on a classification expectation maximization algorithm and applies this algorithm to Ground Penetrating Radar (GPR) spatial data interpretation. Previous work tackling this problem using the Hough transform or neural networks for identifying GPR hyperbolae are unsuitable for on-site applications owing to their computational demands and the difficulties of getting sufficient appropriate training data for neural networkdoi:10.1109/ijcnn.2010.5596298 dblp:conf/ijcnn/ChenC10 fatcat:fwt6ndosbzahrffth4pmil4dsa