Mean shift for accurate number plate detection

Jia, W; Zhang, H; He, X

Mean shift for accurate number plate detection

Jia, W

Zhang, H He, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005, 2005, I pp. 732 - 737
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Proceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005, 2005, I pp. 732 - 737	en_US
dc.identifier.isbn	0769523161	en_US
dc.identifier.isbn	9780769523163	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2368
dc.description.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. © 2005 IEEE.	en_US
dc.relation.ispartof	Proceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005	en_US
dc.title	Mean shift for accurate number plate detection	en_US
dc.type	Conference Proceeding
utslib.citation.volume	I	en_US
utslib.for	080106 Image Processing	en_US
utslib.for	080107 Natural Language Processing	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Sydney, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	I	en_US

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. © 2005 IEEE.

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http://hdl.handle.net/10453/2368