Mean shift for accurate number plate detection

Wenjing Jia, Huaifeng Zhang, Xiangjian He

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

This paper presents a robust method for number plate detection, where mean shift segmentation is used to segment color vehicle images into candidate regions. Three features are extracted in order to decide whether a candidate region contains a number plate, namely, rectangularity, aspect ratio, and edge density. Then, the Mahalanobis classifier is used with respect to the above three features to detect number plate regions accurately. The experimental results show that our algorithm produces high robustness and accuracy.

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005
Pages732-737
Number of pages6
Publication statusPublished - 2005
Externally publishedYes
Event3rd International Conference on Information Technology and Applications, ICITA 2005 - Sydney, Australia
Duration: 4 Jul 20057 Jul 2005

Publication series

NameProceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005
VolumeI

Conference

Conference3rd International Conference on Information Technology and Applications, ICITA 2005
Country/TerritoryAustralia
CitySydney
Period4/07/057/07/05

ASJC Scopus subject areas

  • Engineering (all)

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