@InProceedings{sulc-cvppp2014,
  UPDATE  = { 2015-06-26 },
  IS = { zkontrolovano 26 Jun 2015 },
  author =      {{\v S}ulc, Milan and Matas, Ji{\v r}{\'\i}},
  affiliation = {13133-13133},
  title =       {Texture-Based Leaf Identification},
  year =        {2015},
  pages =       {181-196},
  booktitle =   {Computer Vision - ECCV 2014 Workshops, Part {IV}},
  editor =      {Agapito, Lourdes and Bronstein, Michael M. and Rother, Carsten},
  publisher =   {Springer},
  address =     {Cham, Switzerland},
  isbn =        {978-3-319-16219-5},
  book_pages =  {374},
  volume =      {8928},
  series =      {LNCS},
  month =       {September},
  day =         {6-12},
  venue =       {Zurich, Switzerland},
  annote =      {A novel approach to visual leaf identification is
    proposed. A leaf is represented by a pair of local feature
    histograms, one computed from the leaf interior, the other from
    the border. The histogrammed local features are an improved
    version of a recently proposed rotation and scale invariant
    descriptor based on local binary patterns (LBPs).  Describing the
    leaf with multi-scale histograms of rotationally invariant
    features derived from sign- and magnitude-LBP provides a desirable
    level of invariance. The representation does not use colour.
    Using the same parameter settings in all experiments and standard
    evaluation protocols, the method outperforms the state-of-the-art
    on all tested leaf sets - the Austrian Federal Forests dataset,
    the Flavia dataset, the Foliage dataset, the Swedish dataset and
    the Middle European Woods dataset - achieving excellent
    recognition rates above 99%.  Preliminary results on images from
    the north and south regions of France obtained from the
    LifeCLEF'14 Plant task dataset indicate that the proposed method
    is also applicable to recognizing the environmental conditions the
    plant has been exposed to.},
  keywords =    {Computer Vision, Plants, Recognition, Leaf, Texture},
  project =     {GACR P103/12/G084, SGS13/142/OHK3/2T/13},
  doi =         {10.1007/978-3-319-16220-1_14},
}