IS = { zkontrolovano 09 Dec 2005 },
  UPDATE  = { 2005-09-19 },
 author =      {Matas, Ji{\v r}{\' i} and Zimmermann, Karel  },
 title =       {Unconstrained Licence Plate Detection},
 year =        {2005},
 pages =       {572--577},
 booktitle =   {8th International IEEE Conference on Intelligent Transportation Systems},
 editor =      {Reinhard Pfliegl},
 address =     {Medison, US},
 month =       {September},
 day =         {12-16},
 venue =       {Wien, Austria},
 annote = {Licence plates and traffic signs detection and recognition
   have a number of different applications relevant for transportation
   systems, such as traffic monitoring, detection of stolen vehicles,
   driver navigation support or any statistical research. A number of
   methods have been proposed, but only for particular cases and
   working under constraints (e.g. known text direction or high

   Therefore a new class of locally threshold separable detectors
   based on extremal regions, which can be adapted by machine learning
   techniques to arbitrary shapes, is proposed. In the test set of
   licence plate images taken from different viewpoints
   <-45dg.,45dg.>, scales (from seven to hundreds of pixels height)
   even in bad illumination conditions and partial occlusions, the
   high detection accuracy is achieved (95%). Finally we present the
   detector generic abilities by traffic signs detection.

   The standard classifier (neural network) 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 and partial occlusions. Robustness to a viewpoint change is
   achieved by using invariant descriptors and/or by modelling shape
   variations by the classifier.

   The time-complexity of the detection is approximately linear in the
   number of pixel and a non-optimized implementation runs at about 1
   frame per second for a 640x480 image on a high-end PC.},
 keywords =    {Licence Plate detection, Affine invariant, Object recognition, 
                distinguished regions, CSER, extremal
                regions, MSER, machine learning},
 prestige =    {international},
 authorship =  {50-50},
 project =     {1ET101210407, CONEX GZ 45.535},
 psurl       = {[PDF] },
key         = { Matas-ITSC-2005 },
isbn        = { 0-7803-9216-7 },
publisher   = { IEEE Inteligent Transportation Systems Society },
book_pages  = { 1187 },