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Moire methods are optical methods that are based on the effect of superposition of grating lines and have been widely used in the context of industrial applications for shape analysis, for non-contact measurements, and for quality control of industrial components. In applications the following computations: image filtering, fringe skeletonizing and fringe numbering have to be performed for each test object, before comparison between the numerically reconstructed test object shape and its CAD model. In order to reduce the computing time required by the preceding computations, the inverse moire technique has been introduced by Harthong. Instead of using a grating made of parallel straight lines, the inverse moire technique uses a pre-computed specific gratin, that is formed of curved lines such that the moire pattern is composed of parallel straight fringes if the test object shape is conformed to its CAD model. Defects are then characterized by a deformation and a curvature of these parallel fringes. In this paper, we present examples showing that standard fringe extraction by automatic thresholding is not that easy. To overcome this difficulty, we propose a four stage process algorithmical approach that allows fringe detection in inverse moire images with high sensitivity and specificity. First we used the well-known image processing technique called unsharp masking, to enhance moire image and to emphasize low contrasted fringes. The second step is to extract bright fringes by image segmentation and constrained contour modeling. After detection of these bright fringes inside the zone of interest of the moire image, we get the thick skeleton of adjacent background and of dark fringes. The third step is to skeletonize this thick skeleton of adjacent background and of dark fringes, using morphological thinning of well-composed sets, that assures that each fringe skeleton will be one pixel thick, at the difference of standard thinning techniques. The fourth step is to apply a graph technique to isolate the individual dark fringes. When all these four steps have been followed, one is left with a binary image showing the dark fringe pattern skeleton. The experimental results that have been obtained have shown the robustness of this algorithmical approach, for the analysis of noisy inverse moire images.
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