IS = { zkontrolovano 29 May 2014 },
  UPDATE  = { 2014-02-18 },
  author =      {Ma{\v c}{\'a}k, Jan and Drbohlav, Ond{\v r}ej},
  language =    {English},
  title =       {Efficient inference of spatial hierarchical models},
  year =        {2014},
  book_pages =       {716},
  booktitle =   {VISAPP '14: Proceedings of the 9th International Conference on Computer Vision Theory and Applications},
  editor =      {Battiato, Sebastiano and Braz, Jos{\'{e}}},
  publisher =   {SciTePress - Science and Technology Publications},
  address =     {Porto, Portugal},
  isbn =        {978-989-758-003-1},
  volume =      {1},
  pages =  {500-506},
  month =       {January},
  day =         {5-8},
  venue =       {Lisbon, Portugal},
  organization ={The Institute for Systems and Technologies of Information, Control and Communication (INSTICC)},
  annote =      {The long term goal of artificial intelligence and
                  computer vision is to be able to build models of the
                  world automatically and to use them for
                  interpretation of new situations. It is natural that
                  such models are efficiently organized in a
                  hierarchical manner; a model is build by sub-models,
                  these sub-models are again build of another models,
                  and so on. These building blocks are usually
                  shareable; different objects may consist of the same
                  components. In this paper, we describe a
                  hierarchical probabilistic model for visual domain
                  and propose a method for its efficient inference
                  based on data partitioning and dynamic
                  programming. We show the behaviour of the model,
                  which is in this case made manually, and inference
                  method on a controlled yet challenging dataset
                  consisting of rotated, scaled and occluded
                  letters. The experiments show that the proposed
                  model is robust to all above-mentioned aspects.},
  keywords =    {graphical models, inference},
  prestige =    {international},
  project =     {GACR P103/12/1578},