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Optical surface inspection: A novelty detection approach based on CNN-encoded texture features
Author(s): Michael Grunwald; Matthias Hermann; Fabian Freiberg; Pascal Laube; Matthias O. Franz
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Paper Abstract

In inspection systems for textured surfaces, a reference texture is typically known before novel examples are inspected. Mostly, the reference is only available in a digital format. As a consequence, there is no dataset of defective examples available that could be used to train a classifier. We propose a texture model approach to novelty detection. The texture model uses features encoded by a convolutional neural network (CNN) trained on natural image data. The CNN activations represent the specific characteristics of the digital reference texture which are learned by a one-class classifier. We evaluate our novelty detector in a digital print inspection scenario. The inspection unit is based on a camera array and a flashing light illumination which allows for inline capturing of multichannel images at a high rate. In order to compare our results to manual inspection, we integrated our inspection unit into an industrial single-pass printing system.

Paper Details

Date Published: 17 September 2018
PDF: 13 pages
Proc. SPIE 10752, Applications of Digital Image Processing XLI, 107521E (17 September 2018); doi: 10.1117/12.2320657
Show Author Affiliations
Michael Grunwald, Univ. of Applied Sciences Konstanz (Germany)
Matthias Hermann, Univ. of Applied Sciences Konstanz (Germany)
Fabian Freiberg, Univ. of Applied Sciences Konstanz (Germany)
Pascal Laube, Univ. of Applied Sciences Konstanz (Germany)
Matthias O. Franz, Univ. of Applied Sciences Konstanz (Germany)


Published in SPIE Proceedings Vol. 10752:
Applications of Digital Image Processing XLI
Andrew G. Tescher, Editor(s)

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