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The system achieved is an integration of the identification system, we planned and carried out, as we previously described (Niola et al., 1999). From the depth map onwards, that is the output of the identification system, the classification system, operating on the single lines of the map, reconstructs through interpolation the level lines of the image, which are to be fitted to Fuzzy set; in order to do this, a mixed neuro-fuzzy technique is adopted. The so achieved input Fuzzy sets, level lines that have been modelled, are compared to the output Fuzzy sets, which reproduce a parameterisation of the defect according to its depth, by means of inference rules planned on purpose. The final result will give a quantification of the defect found, besides pointing out the area in which it has been noticed.
We have worked out an innovative system for the identification of superficial defects in low critical metallic patches. In a few seconds we reconstruct, using the fundamental principles of shape from shading, the third dimension of a digitized image, thanks to which we can identify the presence of a defect. Testing shows that the results achieved are more than satisfactory also in relation to the good rejection of the noises from the algorithm. Moreover we propose the advantages of using this system in an industrial field, and the conditions to be verified in industry in order to be able to test this technique.
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