@InProceedings{mishkin-ivcnz13,
  IS = { zkontrolovano 25 Jan 2014 },
  UPDATE  = { 2014-01-06 },
  key =         {mishkin-ivcnz13},
author =      {Mishkin, Dmytro and Per{\'{d}}och, Michal and 
               Matas, Ji{\v{r}}{\'{\i}}},
title =       {Two-view Matching with View Synthesis Revisited},
  year =        {2013},
  pages =       {448-453},
  booktitle =   {2013 28th International Conference of Image and Vision Computing New Zealand (IVCNZ 2013)},
  editor =      {Taehyun Rhee, Ramesh Rayudu, Christopher Hollitt, John Lewis, Mengjie Zhang},
  publisher =   {IEEE},
  address =     {IEEE Operations Center, 445 Hoes Lane, Piscataway, USA},
  isbn =        {978-1-4799-0882-0},
  book_pages =  {517},
  month =       {November},
  day =         {27-29},
  venue =       {Wellington, New Zealand},
  annote =      {Wide-baseline matching focussing on problems with
                  extreme viewpoint change is considered. We in
                  troduce the use of view synthesis with
                  affine-covariant detectors to solve such problems
                  and show that matching with the Hessian-Affine or
                  MSER detectors outperforms the state-of-the-art
                  ASIFT [19].  To minimise the loss of speed caused by
                  view synthesis, we propose the Matching On Demand
                  with view Synthesis algorithm (MODS) that uses
                  progressively more synthesized images and more
                  (time-consuming) detectors until reliable estimation
                  of geometry is possible. We show experimentally that
                  the MODS algorithm solves problems beyond the
                  state-of-the-art and yet is comparable in speed to
                  standard wide-baseline matchers on simpler problems.
                  Minor contributions include an improved method for
                  tentative correspondence selection, applicable both
                  with and without view synthesis and a view synthesis
                  setup greatly improving MSER robustness to blur and
                  scale change that increase its running time by 10%
                  only.},
  keywords =    {feature extraction, image matching, view synthesis},
  project =     {FP7-ICT-270138 DARWIN, TACR TE01020415 V3C,  ERC-CZ LL1303},
}