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.