IS = { zkontrolovano 13 Dec 2007 },
  UPDATE  = { 2007-07-24 },
  author =      {Kostliv{\'a}, Jana and {\v C}ech, Jan and {\v S}{\'a}ra, Radim},
  title =       {{ROC} Based Evaluation of Stereo Algorithms},
  institution = {Center for Machine Perception, K13133 FEE
                 Czech Technical University},
  address =     {Prague, Czech Republic},
  year =        {2007},
  month =       {March},
  type =        {Research Report},
  number =      {CTU--CMP--2007--08},
  issn =        {1213-2365},
  pages =       {25},
  figures =     {8},
  authorship =  {34-33-33},
  psurl       = {[Kostliva-TR-2007-08.pdf]},
  project =     {1ET101210406, FP6-IST-027113 eTRIMS, 
                 MRTN-CT-2004-005439 VISIONTRAIN, Dur IG2003-2 062},
  annote = {Which stereo algorithm is better? The one which is very
    dense but often erroneous or rather one which is very accurate but
    sparse? It depends on the application. In general, we can only say
    that the algorithm is better than the other if it is more accurate
    and denser. In literature, there exist several methods to evaluate
    quality of dense stereo matching algorithms. Their bottleneck is
    in tested algorithm parameter setting, which is assumed to be
    fixed.  Such evaluation results are typically very different for
    various parameter setting in the sense they somehow change the
    tradeoff between accuracy and density. Therefore, we developed a
    new method for testing stereo algorithm based on the ROC-like
    analysis. We introduce ROC curves for stereo algorithms and define
    new numerical characteristics, which evaluate the algorithm
    itself, not a pair (algorithm, parameter setting) as it is in
    literature. Comparing ROC-curves of all tested algorithms we
    obtain the Feasibility Boundary, which is the ROC curve of all
    algorithms together, i.e. the best possible results which are
    feasible by a set of tested stereo algorithms. The important are
    the algorithms which forms the feasibility boundary, since they
    produce the best feasible results. On the other hand the
    algorithms which do not appear in the feasibility boundary are
    worse than the others both in the accuracy and density and are
    redundant in fact. We performed an experiment evaluating several
    known algorithms (representatives of different approaches) on
    several complex scenes with ground-truth disparity maps.
    Surprisingly, from this set, the most of the algorithms appear on
    the feasibility boundary, i.e. they are the best of all for
    certain requirement for density or accuracy.  Based on this study,
    the algorithms with a strong prior models are suitable when higher
    density is desired which causes higher errors. Algorithms with a
    weak prior model but unambiguous, are suitable for application
    where there is a requirement for low error which inevitably causes
    sparser matching results. We are preparing a web-site for an
    automatic evaluation, so that other researchers can easily use
    this method. Such a collection of evaluation results is also
    useful for a potential user, who can simply select the most
    suitable algorithm and read the parameter settings. },
  keywords =    {computer vision, dense stereo, performance evaluation, 
                 ROC analysis},