Abstract
We address a current problem in industrial quality control, the detection of defects in a laser welding process. The process is observed by means of a high-speed camera, and the task is complicated by the fact that very high sensitivity is required in spite of a highly dynamic / noisy background and that large amounts of data need to be processed online. In a first stage, individual images are rated and these results are then aggregated in a second stage to come to an overall decision concerning the entire sequence. Classification of individual images is by means of a polynomial classifier, and both its parameters and the optimal subset of features extracted from the images are optimized jointly in the framework of a wrapper optimization. The search for an optimal subset of features is performed using a range of different sequential and parallel search strategies including genetic algorithms.
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Hader, S., Hamprecht, F.A. (2005). Two-stage Classification with Automatic Feature Selection for an Industrial Application. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_13
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DOI: https://doi.org/10.1007/3-540-28084-7_13
Publisher Name: Springer, Berlin, Heidelberg
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