"8th International IEEE Conference on Intelligent Transportation Systems" . . . . "2"^^ . "Matas, Ji\u0159\u00ED" . . . "6"^^ . . "RIV/68407700:21230/05:03109942" . "547827" . "P(1ET101210407)" . . "Affine invariant; CSER; Licence Plate detection; MSER; Object recognition; distinguished regions; extremal regions; machine learning"@en . . . "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 resolution). 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 region" . . . . "Nen\u00ED k dispozici"@cs . "2005-09-13+02:00"^^ . "21230" . . "Nen\u00ED k dispozici"@cs . . . "RIV/68407700:21230/05:03109942!RIV06-AV0-21230___" . "0-7803-9216-7" . "Wien" . . "Omnipress" . . "Unconstrained Licence Plate Detection"@en . . "Unconstrained Licence Plate Detection" . . "572 ; 577" . "Unconstrained Licence Plate Detection" . "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 resolution). 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 region"@en . "Unconstrained Licence Plate Detection"@en . . "Zimmermann, Karel" . . "Nen\u00ED k dispozici"@cs . "[6A9A87B8712D]" . . "Madison" . "2"^^ .