Introduction of a wavelet transform based on 2D matched filter in a Markov Random Field for fine structure extraction: Application on road crack detection

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.