2 February 2009 Introduction of a wavelet transform based on 2D matched filter in a Markov random field for fine structure extraction: application on road crack detection
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Abstract
In the context of fine structure extraction, lots of methods have been introduced, and, particularly in pavement crack detection. We can distinguish approaches based on a threshold, employing mathematical morphology tools or neuron networks and, more recently, techniques with transformations, like wavelet decomposition. The goal of this paper is to introduce a 2D matched filter in order to define an adapted mother wavelet and, then, to use the result of this multi-scale detection into a Markov Random Field (MRF) process to segment fine structures of the image. Four major contributions are introduced. First, the crack signal is replaced by a more real one based on a Gaussian function which best represents the crack. Second, in order to be more realistic, i.e. to have a good representation of the crack signal, we use a 2D definition of the matched filter based on a 2D texture auto-correlation and a 2D crack signal. The third and fourth improvements concern the Markov network designed in order to allow cracks to be a set of connected segments with different size and position. For this part, the number of configurations of sites and potential functions of the MRF model are completed.
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Sylvie Chambon, Sylvie Chambon, Peggy Subirats, Peggy Subirats, Jean Dumoulin, Jean Dumoulin, } "Introduction of a wavelet transform based on 2D matched filter in a Markov random field for fine structure extraction: application on road crack detection", Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510A (2 February 2009); doi: 10.1117/12.805437; https://doi.org/10.1117/12.805437
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