IS = { zkontrolovano 13 Jan 2005 },
  UPDATE  = { 2004-12-08 },
  author =   {Flach, Boris and {\v S}{\'a}ra, Radim},
  title =   {Joint Non-rigid Motion Estimation and Segmentation},
  year =   {2004},
  pages =   {631-638},
  booktitle =   {IWCIA '04: Proceedings 10th International Workshop on
                  Combinatorial Image Analysis},
  editor =   {Klette, Reinhard and {\v Z}uni{\'c}, Jovisa},
  publisher =   {Springer Verlag},
  address =   {Heidelberg, Germany},
  isbn =   {0302-9743},
  volume =   {3322},
  series =   {LNCS},
  book_pages =   {800},
  month =   {December},
  day =     {1-3},
  venue =   {Auckland, New Zealand},
  annote =   {Usually, object segmentation and motion estimation are
                  considered (and modelled) as different tasks. For motion
                  estimation this leads to problems arising especially at the
                  boundary of an object moving in front of another if
                  e.g. prior assumptions about continuity of the motion field
                  are made. Thus we expect that a good segmentation will
                  improve the motion estimation and vice versa. To demonstrate
                  this, we consider the simple task of joint segmentation and
                  motion estimation of an arbitrary (non-rigid) object moving
                  in front of a still background. We propose a statistical
                  model which represents the moving object as a triangular
                  mesh of pairs of corresponding points and introduce an
                  provably correct iterative scheme, which simultaneously
                  finds the optimal segmentation and corresponding motion
  keywords =   {Computer vision, segmentation, motion estimation, Markov
                  random fields},
  authorship =   {70-30},
  project =   {1ET101210406, Quandt},