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Statements

Subject Item

n2:RIV%2F68407700%3A21230%2F05%3A03109942%21RIV06-AV0-21230___

rdf:type

n19:Vysledek skos:Concept

dcterms:description

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 Není k dispozici 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

dcterms:title

Není k dispozici Unconstrained Licence Plate Detection Unconstrained Licence Plate Detection

skos:prefLabel

Unconstrained Licence Plate Detection Unconstrained Licence Plate Detection Není k dispozici

skos:notation

RIV/68407700:21230/05:03109942!RIV06-AV0-21230___

n3:strany

572 ; 577

n3:aktivita

n15:P

n3:aktivity

P(1ET101210407)

n3:dodaniDat

n5:2006

n3:domaciTvurceVysledku

n6:1711326 n6:6464742

n3:druhVysledku

n11:D

n3:duvernostUdaju

n20:S

n3:entitaPredkladatele

n4:predkladatel

n3:idSjednocenehoVysledku

547827

n3:idVysledku

RIV/68407700:21230/05:03109942

n3:jazykVysledku

n13:eng

n3:klicovaSlova

Affine invariant; CSER; Licence Plate detection; MSER; Object recognition; distinguished regions; extremal regions; machine learning

n3:klicoveSlovo

n7:distinguished%20regions n7:machine%20learning n7:extremal%20regions n7:CSER n7:Object%20recognition n7:Licence%20Plate%20detection n7:MSER n7:Affine%20invariant

n3:kontrolniKodProRIV

[6A9A87B8712D]

n3:mistoKonaniAkce

Wien

n3:mistoVydani

Madison

n3:nazevZdroje

8th International IEEE Conference on Intelligent Transportation Systems

n3:obor

n21:JD

n3:pocetDomacichTvurcuVysledku

2

n3:pocetTvurcuVysledku

2

n3:projekt

n10:1ET101210407

n3:rokUplatneniVysledku

n5:2005

n3:tvurceVysledku

Matas, Jiří Zimmermann, Karel

n3:typAkce

n9:WRD

n3:zahajeniAkce

2005-09-13+02:00

s:numberOfPages

6

n18:hasPublisher

Omnipress

n17:isbn

0-7803-9216-7

n14:organizacniJednotka

21230