@article{Serradell-PAMI2014,
IS          = { zkontrolovano 10 Mar 2015 },
UPDATE   = { 2015-03-10 },
  author = {Serradell, Eduard and Am{\' a}vel Pinheiro, Miguel and
     Sznitman, Raphael and Kybic, Jan and Moreno-Noguer, Francesc and 
     Fua, Pascal},
affiliation = { NULL-13133-NULL-13133-NULL-NULL },
  Title =        {Non-Rigid Graph Registration using Active Testing Search},
  journal =      {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
volume      = { 37 },
number      = { 3 },
year        = { 2015 },
month       = { March },
  publisher =    {IEEE Computer Society},
  address =      {Los Alamitos, USA},
issn        = { 0162-8828 },
pages       = { 625--638 },
  authorship =   {40-40-8-8-2-2},
  Project =      {SGS12/190/OHK3/3T/13, GACR P202/11/0111, SFRH/BD/77134/2011},
  keywords = {Graph matching, Non-rigid registration, Active search},
  annote = {We present a new approach for matching sets of branching
                  curvilinear structure s that form graphs embedded in
                  $\mathbb{R}^2$ or $\mathbb{R}^3$ and may be subject
                  to deformations. Unlike earlier method s, ours does
                  not rely on local appearance similarity nor does
                  require a good initial alignment. Furthermore, it
                  can cope with non-linear deformatio ns, topological
                  differences, and partial graphs. To handle arbitrary
                  non-linear deformations, we use Gaussian Processes
                  to represen t the geometrical mapping relating the
                  two graphs. In the absence of appearance
                  information, we iteratively establish corresp
                  ondences between points, update the mapping
                  accordingly, and use it to estimate where to find
                  the most likely correspondences that will be used in
                  the next step. To make the computation tractable for
                  large graphs, the set of new potential matches
                  consider ed at each iteration is not selected at
                  random as in many RANSAC-based algorithms. Instead,
                  we introduce a so-called Active Testin g Search
                  strategy that performs a priority search to favor
                  the most likely matches and speed-up the process. We
                  demonstrat e the effectiveness of our approach first
                  on synthetic cases and then on angiography data,
                  retinal fundus images, and microsco py image stacks
                  acquired at very different resolutions.},
Psurl    = { [Serradell-PAMI2015.pdf] },
Url      = { ftp://cmp.felk.cvut.cz/pub/cmp/articles/amavemig/Serradell-PAMI2015.pdf },
doi         = { 10.1109/TPAMI.2014.2343235 },
}