IS = { zkontrolovano 12 Jan 2009 },
  UPDATE  = { 2008-11-27 },
  author   = { Shekhovtsov, Alexander and Kovtun, Ivan and 
                Hlav{\' a}{\v c}, V{\' a}clav },
  title =      {Efficient {MRF} Deformation Model for Non-Rigid Image Matching},
  pages =      {91-99},
  journal =    {Computer Vision and Image Understanding},
  volume =     {112},
  year =       {2008},
  publisher =  {Elsevier},
  address =    {San Diego, USA},
  issn =       {1077-3142},
  annote = {We propose a novel MRF-based model for deformable image
    matching (also known as registration). The deformation is
    described by a field of discrete variables, representing
    displacements of (blocks of) pixels. Discontinuities in the
    deformation are prohibited by imposing hard pairwise constraints
    in the model. Exact maximum a posteriori inference is intractable
    and we apply a linear programming relaxation technique.  We show
    that, when reformulated in the form of two coupled fields of x-
    and y- displacements, the problem leads to a simpler relaxation to
    which we apply the TRW-S (Sequential Tree-Reweighted Message
    passing) algorithm [Wainwright-03, Kolmogorov-05]. This enables
    image registration with large displacements at a single scale. We
    employ fast message updates for a special type of interaction as
    was proposed [Felzenszwalb and Huttenlocher-04] for the
    max-product Belief Propagation (BP) and introduce a few
    independent speedups. In contrast to BP, the TRW-S allows us to
    compute per-instance approximation ratios and thus to evaluate the
    quality of the optimization. The performance of our technique is
    demonstrated on both synthetic and real-world experiments.},
  keywords =    {Markov random fields, MRF, message passing, labeling, 
                 image registration, energy minimization},
  authorship =  {70-10-20},
  project =     {ICT-215078 DIPLECS, MSM6840770038},
  prestige    = {important},
  month       = { October },
  number      = { 1 },