Pattern recognition of industrial defects by multiresolution analysis with wavelet decomposition

摘要

Abstract: The purpose of this paper is to present a method of pattern recognition applied to detect discrimination in objects manufactured in plastic, metal, glass... This discrimination is needed to avoid problems during the recycling process. Nowadays, the controls are realized by an operator who checks visually these objects. As in texture segmentation, a way to limit the data which much be analyzed, is to use orthogonal transformations. In an industrial background, one of the most interesting transformations is the orthogonal wavelet decomposition. Remaining in the image vector space, this decomposition allows a multi resolution analysis and keeps quite all the original information in the subimages. Applied to industrial objects presenting a complex textured aspect, all the wavelets (Haar, bi-orthogonal...) need post- processing to display the defects. As these defects are seen like texture breakdowns, they can be located in high frequency spatial domain. This has led us to choose Daubechies wavelets that concentrate correctly the useful information in the detail subimages. We show that the defect is more clearly apparent at a given resolution level than in the original image. We give criteria that allow the determination of this optimal resolution level. We present a method that allows the reconstruction of the defect, using the subimages. The defect, appearing on a black background, is then discriminated by an adapted classical pattern recognition method.!18