IS = { zkontrolovano 27 Jan 2009 },
  UPDATE  = { 2008-12-29 },
  author =	 {Drbohlav, Ond{\v r}ej and Chantler, Mike},
  title =	 {Illumination-invariant texture classification using
                  single training images},
  booktitle =	 {Texture 2005: Proceedings of the 4th international
                  workshop on texture analysis and synthesis},
  pages =	 {31-36},
  year =	 {2005},
  isbn =	 {1-904410-13-8},
  book_pages =	 {163},
  editor =	 {Chatler, Mike and Drbohlav, Ond{\v r}ej},
  publisher =    {Heriot-Watt University},
  address =      {Edinburgh, UK},
  venue =	 {Beijing, China},
  annote = { The appearance of a surface texture is highly dependent
    on illumination. This is why current surface texture
    classification methods require multiple training images captured
    under a variety of illumination conditions for each class. We show
    that a single training image per class can be sufficient if the
    surfaces are of uniform albedo and smooth and shallow relief, and
    the illumination is sufficiently far from the texture
    macro-normal. The performance of our approach is demonstrated on
    classification of 20 textures in the PhoTex database. For test
    images which are most different from the training images
    (different instances of the same texture observed, non-equal
    illumination slants), the success rate achieved is in the range of
    60-80\%. When the test images differ from the training ones only
    in illumination tilt, the success rate achieved is well above
    95\%. },
project     = { UK_Photex },