@inproceedings{GrunwaldHermannFreibergetal.2018,
  author    = {Grunwald, Michael and Hermann, Matthias and Freiberg, Fabian and Laube, Pascal and Franz, Matthias O.},
  title     = {Optical surface inspection: A novelty detection approach based on CNN-encoded texture features},
  booktitle = {Applications of Digital Image Processing XLI, 19-23 August 2018, San Diego, California (Proceedings of SPIE Optical Engineering \& Applications, Vol. 10752)},
  doi       = {10.1117/12.2320657},
  institution = {Institut f{\"u}r Optische Systeme - IOS},
  pages     = {13},
  year      = {2018},
  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.},
  language  = {en}
}