IS = { zkontrolovano 02 Dec 2005 },
  UPDATE  = { 2005-07-18 },
  author =      {Matas, Jiri and Zimmermann, Karel },
  title =       {A New Class of Learnable Detectors for Categorisation},
  year =        {2005},
  pages =       {541--550},
  booktitle =   {SCIA '05: Proceedings of the 14th Scandinavian Conference on
                  Image Analysis},
  editor =      {Kalviainen, Heikki and Parkkinen, Jussi and Kaarna, Arto},
  publisher =   {Springer-Verlag },
  address =     {Heidelberg, Germany },
  isbn =        {0302-9743 },
  volume =      {1},
  series =      {LNCS},
  number =      {3540},
  book_pages =  {1270},
  month =       {June},
  day =         {19-22},
  venue =       {Joensuu, Finland},
  annote = {A new class of image-level detectors that can be adapted
    by machine learning techniques to detect parts of objects from a
    given category is proposed. A classifier (e.g. neural network or
    adaboost) within the detector selects a relevant subset of
    extremal regions, i.e. regions that are connected components of a
    thresholded image. Properties of extremal regions render the
    detector very robust to illumination change.  Robustness to
    viewpoint change is achieved by using invariant descriptors and/or
    by modelling shape variations by the classifier. The approach is
    brought to bear on three problems: text detection, face
    segmentation and leopard skin detection. High detection rates were
    obtained for unconstrained (i.e. brihtness, affine and font
    invariant) text detection (92%) with reasonable false positive
    rate. The time-complexity of the detection is approximately linear
    in the umber of pixel and a non-optimized implementation runs at
    about 1frame per second for a 640x480 image on a high-end
  keywords =    {Object recognition, distinguished regions, CSER, 
                 extremal regions, MSER, machine learning},
  prestige =    {international},
  authorship =  {50-50},
  project =     {1ET101210407, IST-004176},
  psurl       = {[PDF] },