@inproceedings{2000138,
author={Amir Monadjemi and  Majid Mirmehdi and Barry Thomas},
title={Restructured Eigenfilter Matching for Novelty Detection in Random Textures},
booktitle={Proceedings of the 15th British Machine Vision Conference},
ISBN={1-901725-25-1},
publisher={BMVA Press},
pages={637--646},
month={September},
year={2004},
abstract={
A new eigenfilter-based novelty detection approach to find abnormalities
in random textures is presented. The proposed
algorithm reconstructs a given texture twice using a subset of its own
eigenfilter bank and a subset of a reference (template) eigenfilter bank, and
measures the reconstruction error as the level of novelty. We then present an
improved reconstruction generated by structurally matched eigenfilters through
rotation, negation, and mirroring. We apply the method to the detection of
defects in textured ceramic tiles. The method is over $90\%$ accurate, and is
fast and amenable to implementation on a production line.},
abstract-url={http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2000138},
url={http://www.cs.bris.ac.uk/Publications/Papers/2000138.pdf},
keyword={Computer Vision},
pubtype={102}
}