IS = { zkontrolovano 13 Apr 2012 },
  UPDATE  = { 2012-04-13 },
  author    = { Zhao, Guoying and                                                                                                                                 
                Ahonen, Timo and                                                                                                                                 
                Matas, Ji{\v r}{\'\i} and                                                                                                                        
                Pietik{\" a}inen, Matti },
  title =     { Rotation-Invariant Image and Video Description With Local
                Binary Pattern Features },
  journal =   { {IEEE} Transactions on Image Processing},
  volume =    { 21 },
  number =    { 4 },
  year =      { 2012 },
  month =     { April },
  publisher = { IEEE Signal Processing Society },
  address =   { New Jersey , USA },
  issn =      { 1057-7149 },
  pages     = { 1465--1477 },
  authorship ={ 25-25-25-25},
  project   = { GACR P103/10/1585},
  doi       = { 10.1109/TIP.2011.2175739 },
  keywords  = { texture classification, Fourier transform, dynamic texture,
                local binary patterns (LBP) , rotation invariance},
  psurl =     { http://80.ieeexplore.ieee.org.dialog.cvut.cz/stamp/stamp.jsp?tp=&arnumber=6078431},
  annote = { In this paper, we propose a novel approach to compute
   rotation-invariant features from histograms of local noninvariant
   patterns. We apply this approach to both static and dynamic local
   binary pattern (LBP) descriptors. For static-texture description, we
   present LBP histogram Fourier (LBP-HF) features, and for
   dynamic-texture recognition, we present two rotation-invariant
   descriptors computed from the LBPs from three orthogonal planes
   (LBP-TOP) features in the spatiotemporal domain. LBP-HF is a novel
   rotation-invariant image descriptor computed from discrete Fourier
   transforms of LBP histograms. The approach can be also generalized to
   embed any uniform features into this framework, and combining the
   supplementary information, e.g., sign and magnitude components of the
   LBP, together can improve the description ability. Moreover, two
   variants of rotation-invariant descriptors are proposed to the
   LBP-TOP, which is an effective descriptor for dynamic-texture
   recognition, as shown by its recent success in different application
   problems, but it is not rotation invariant. In the experiments, it is
   shown that the LBP-HF and its extensions outperform noninvariant and
   earlier versions of the rotation-invariant LBP in the
   rotation-invariant texture classification. In experiments on two
   dynamic-texture databases with rotations or view variations, the
   proposed video features can effectively deal with rotation variations
   of dynamic textures (DTs). They also are robust with respect to
   changes in viewpoint, outperforming recent methods proposed for
   view-invariant recognition of DTs. },
  ut_isi    = {000302181800004},