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Probabilistic robust hyperbola mixture model for interpreting ground penetrating radar data
2010
The 2010 International Joint Conference on Neural Networks (IJCNN)
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 network
doi:10.1109/ijcnn.2010.5596298
dblp:conf/ijcnn/ChenC10
fatcat:fwt6ndosbzahrffth4pmil4dsa