IS = { zkontrolovano 08 Aug 2014 },
  UPDATE  = { 2014-08-05 },
  author =       {Kybic, Jan},
  title =        {Registration of segmented histological images using
                  thin plate splines and belief propagation},
  booktitle =    {SPIE Medical Imaging},
  year =         {2014},
  authorship =   {100},
  project =      {GACR P202/11/0111},
  book_pages =   {1250},
  address={Bellingham, Washington},
  day =          {15--20},
  editor={Ourselin, S. and Styner, M.},
  venue={San Diego, CA, U.S.A.},
  isbn =         {9780819498274},
  url = {ftp://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Kybic-SPIEMI2014.pdf},
  annote={ We register images based on their multiclass segmentations,
                  for cases when correspondence of local features
                  cannot be established. A discrete mutual information
                  is used as a similarity criterion. It is evaluated
                  at a sparse set of location on the interfaces
                  between classes.  A thin-plate spline regularization
                  is approximated by pairwise interactions. The
                  problem is cast into a discrete setting and solved
                  efficiently by belief propagation. Further speedup
                  and robustness is provided by a multiresolution
                  framework.  Preliminary experiments suggest that our
                  method can provide similar registration quality to
                  standard methods at a fraction of the computational
  keywords={image registration, segmentation},