IS = { zkontrolovano 01 Aug 2011 },
 UPDATE  = { 2011-08-01 },
 journal =      {Computer Vision and Image Understanding},
 author =       {Kybic, Jan and Nieuwenhuis, Claudia},
 title =        {Bootstrap Optical Flow Confidence and Uncertainty Measure},
 institution =  {Department of Cybernetics, Faculty of Electrical Engineering,
                 Czech Technical University},
 year =         {2011},
 month =        {October},
 volume =       {115},
 authorship =   {90-10},
 project =      {MSM6840770012},
 annote =       {We address the problem of estimating the uncertainty
   of optical flow algorithm results. Our method estimates the error
   magnitude at all points in the image. It can be used as a
   confidence measure. It is based on bootstrap resampling, which is a
   computational statistical inference technique based on repeating
   the optical flow calculation several times for different randomly
   chosen subsets of pixel contributions. As few as 10 repetitions are
   enough to obtain useful estimates of geometrical and angular
   errors. We use the combined local global optical flow method (CLG)
   which generalizes both Lucas-Kanade and Horn-Schunck type
   methods. However, the bootstrap method is very general and can be
   applied to almost any optical flow algorithm that can be formulated
   as a minimization problem. We show experimentally on synthetic as
   well as real video sequences with known ground truth that the
   bootstrap method performs better than all other confidence measures
  psurl = {{[1.2MB, pdf]}},
  url = {{ftp://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Kybic-CVIU2010.pdf}},
  keywords =     {optical flow, bootstrap, confidence measure, 
                  motion estimation, uncertainty estimation},
  pages = {1449--1462},
  number = {10},
  doi = {10.1016/j.cviu.2011.06.008},
  if = {2.4},
  issn = {1077-3142},
  publisher = {Elsevier},
  address = {San Diego, USA},