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Proceedings Paper

Introduction of a wavelet transform based on 2D matched filter in a Markov random field for fine structure extraction: application on road crack detection
Author(s): Sylvie Chambon; Peggy Subirats; Jean Dumoulin
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Paper 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.

Paper Details

Date Published: 2 February 2009
PDF: 12 pages
Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510A (2 February 2009); doi: 10.1117/12.805437
Show Author Affiliations
Sylvie Chambon, Lab. Central des Ponts et Chaussées (France)
Peggy Subirats, Ctr. d’Études Techniques de l’Équipement (France)
Jean Dumoulin, Lab. Central des Ponts et Chaussées (France)

Published in SPIE Proceedings Vol. 7251:
Image Processing: Machine Vision Applications II
Kurt S. Niel; David Fofi, Editor(s)

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