IS = { zkontrolovano 13 Dec 2007 },
  UPDATE  = { 2007-07-24 },
 author =      {{\v C}ech, Jan and {\v S}{\'a}ra, Radim},
 title =       {Efficient Sampling of Disparity Space for 
                Fast and Accurate Matching},
 year =        {2007},
 pages =       {8},
 booktitle =   {BenCOS 2007: CVPR Workshop Towards Benchmarking
                Automated Calibration, Orientation and Surface
                Reconstruction from Images},
 publisher =   {Omnipress},
 address =     {Madison, USA},
 isbn =        {1-4244-1180-7},
 month =       {June},
 day =         {23},
 venue =       {Minneapolis, USA},
 organization ={IEEE},
 annote = {A simple stereo matching algorithm is proposed that visits
   only a small fraction of disparity space in order to find a
   semi-dense disparity map. It works by growing from a small set of
   correspondence seeds. Unlike in known seedgrowing algorithms, it
   guarantees matching accuracy and correctness, even in the presence
   of repetitive patterns. This success is based on the fact it solves
   a global optimization task. The algorithm can recover from wrong
   initial seeds to the extent they can even be random. The quality of
   correspondence seeds influences computing time, not the quality of
   the final disparity map. We show that the proposed algorithm
   achieves similar results as an exhaustive disparity space search
   but it is two orders of magnitude faster. This is very unlike the
   existing growing algorithms which are fast but erroneous. Accurate
   matching on 2-megapixel images of complex scenes is routinely
   obtained in a few seconds on a common PC from a small number of
   seeds, without limiting the disparity search range.},
 keywords =    {stereo, matching, growing, random, correspondence, seeds, robust, stable, repetitve, pattern},
 prestige =    {international},
 authorship =  {50-50},
 project =     {FP6-IST-027113, MRTN-CT-2004-005439, 1ET101210406, 
                Dur IG2003-2 062},
 psurl       = {[PDF] },
 note = {CD-ROM},