Assessment of the influence of adaptive components in trainable surface inspection systems |
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Authors: | Christian Eitzinger W Heidl E Lughofer S Raiser JE Smith MA Tahir D Sannen H Van Brussel |
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Affiliation: | 1. Profactor GmbH, Steyr, Austria 2. Johannes Kepler University, Linz, Austria 3. University of the West of England, Bristol, UK 4. Katholieke Universiteit Leuven, Leuven, Belgium
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Abstract: | In this paper, we present a framework for the classification of images in surface inspection tasks and address several key
aspects of the processing chain from the original image to the final classification result. A major contribution of this paper
is a quantitative assessment of how incorporating adaptivity into the feature calculation, the feature pre-processing, and
into the classifiers themselves, influences the final image classification performance. Hereby, results achieved on a range
of artificial and real-world test data from applications in printing, die-casting, metal processing and food production are
presented. |
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Keywords: | |
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