@InProceedings{Tylecek-GCPR2013,
  IS = { zkontrolovano 24 Jan 2014 },
  UPDATE  = { 2013-09-19 },
  year =       {2013},
  month =      {September},
  day =        {3-6},
  venue =      {Saarbruecken, Germany},
  isbn =       {978-3-642-40601-0},
  booktitle =  {GCPR 2013: Proceedings of 35th German 
                Conference on Pattern Recognition},
  volume =     {8142},
  series =     {Lecture Notes in Computer Science},
  editor =     {Weickert, Joachim and Hein, Matthias and Schiele, Bernt},
  doi =        {10.1007/978-3-642-40602-7_39},
  title =      {Spatial Pattern Templates for Recognition of Objects 
                with Regular Structure},
  publisher =  {Springer},
  author =     {Radim Tyle{\v c}ek and Radim {\v S}{\'a}ra},
  pages =      {364-374},
  address =    {Heidelberg, Germany },
  book_pages = {448},
  annote = {We propose a method for semantic parsing of images with
    regular structure.  The structured objects are modeled in a
    densely connected CRF.  The paper describes how to embody specific
    spatial relations in a representation called Spatial Pattern
    Templates(SPT), which allows us to capture regularity constraints
    of alignment and equal spacing in pairwise and ternary potentials.
    Assuming the input image is pre-segmented to salient regions the
    SPT describe which segments could interact in the structured
    graphical model.  The model parameters are learnt to describe the
    formal language of semantic labelings.  Given an input image, a
    consistent labeling over its segments linked in the CRF is
    recognized as a word from this language.  The CRF framework allows
    us to apply efficient algorithms for both recognition and
    learning. We demonstrate the approach on the problem of facade
    image parsing and show that results comparable with state of the
    art methods are achieved without introducing additional manually
    designed detectors for specific terminal objects.},
  keywords = {computer vision, pattern recognition, regular structure, 
              template, graphical models, facade parsing},
  project =  {GACR P103/12/1578},
  prestige =     {international},
  authorship =   {50-50},
  ut_isi =       {},
  psurl =  {[preprint, PDF]},
  acceptance_ratio ={40/79},
}