@TechReport{Sulc-TR-2014-10,
  IS = { zkontrolovano 08 Aug 2014 },
  UPDATE  = { 2014-08-05 },
  author =	 {{\v S}ulc, Milan and Matas, Ji{\v r}{\'\i}},
  title =	 {Texture-Based Leaf Identification},
  institution =	 {Center for Machine Perception, K13133 FEE Czech Technical
                  University},
  address =	 {Prague, Czech Republic},
  year =	 {2014},
  month =	 {July},
  type =	 {Research Report},
  number =	 {CTU--CMP--2014--10},
  issn =	 {1213-2365},
  pages =	 {20},
  figures =	 {10},
  authorship =	 {50-50},
  psurl =	 {[
Sulc-TR-2014-10.pdf]},
  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 d ataset, the
                  Flavia dataset, the Foliage dataset, the Swedish
                  dataset and the Midd le European Woods dataset -
                  achieving excellent recognition rates above 99% .
                  Preliminary results on images from the jnorth and
                  south regions of Franc e obtained from the
                  LifeCLEF'14 Plant task dataset indicate that the
                  propos ed method is also applicable to recognizing
                  the environmental conditions the plant has been
                  exposed to.},
  keywords =	 {Computer Vision, Recognition, Leaf, Leaves, Ffirst, Texture},
  project =     {GACR P103/12/G084,SGS13/142/OHK3/2T/13},
}