Feature extraction and decision procedure for automated inspection of textured materials |
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Authors: | Michael Unser Frank Ade |
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Affiliation: | Signal Processing Laboratory, Swiss Federal Institute of Technology, EPF-Lausanne, 16 chemin de Bellerive, CH-1007 Lausanne, Switzerland;Institute for Communication Technology, Swiss Federal Institute of Technology, ETH-Zentrum, Gloriastr. 35, CH-8092 Zürich, Switzerland |
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Abstract: | This paper proposes a general system approach applicable to the automatic inspection of textured material. First, the input image is preprocessed in order to be independent of non-uniformities. A tone-to-texture transform is then performed by mapping the original grey level picture on a multivariate local feature sequence, which turns out to be normally distributed. More specifically, features derived with the help of the Karhunen-Loève decomposition of a small neighbourhood of each pixel are used. A decision as to conformity with a reference texture is arrived at by thresholding the Mahalanobis distance for every realization of the feature vector. It is shown that this approach is optimum under the Gaussian assumption in the sense that it has a minimum acceptance region for a fixed probability of false rejection. |
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Keywords: | Automated inspection texture analysis feature extraction decision theory Karhunen-Loève transform eigenfilters |
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