@MastersThesis{Kosata-CAK-2007-26,
  author =       {Ko{\v s}ata, Jan},
  supervisor =   {Svoboda, Tom{\'a}{\v s}},
  title =        {Automatic Segmentation of Articulated Objects of
                  Unknown Complexity from Videos},
  school =       {Department of Cybernetics, Faculty of Electrical,
                  Engineering Czech Technical University},
  address =      {Prague, Czech Republic},
  year =         {2007},
  month =        {February},
  day =          {15},
  type =         {{MSc Thesis K333--26/07, CTU--CMP--2007--03}},
  pages =        {40},
  authorship =   {100},
  psurl =        {[Kosata-TR-2007-03.pdf]},
  project =      {1M0567, 1ET101210407},
  annote = {Motion segmentation is an important topic in computer
    vision. Unlike sim- ple motion segmentation articulated
    segmentation gives much more information about observed scene and
    makes easier classification of the observed object, i.e. by
    kinetic patterns. We are interested in automatic segmentation of
    an artic- ulated object with an unknown complexity. Typical
    representative is a human being or a more complicated machine. We
    implemented the method described in M. Pawan Kumar, P.H.S. Torr a
    A. Zisserman - Learning Layered Motion Segmentation of Video. This
    algorithm is applicable to any video containing piecewise rigid
    motion. Given the sequence, the generative model which best
    describes an articulated object and a background is learnt in an
    unsupervised manner. The generative model for a layered
    representation describes the scene as a composition of layers. },
  keywords =     {computer vision, motion segmentation, layered
                  generative model, articulated model},
}