Operator context scanning to support high segmentation rates for real time license plate recognition

Giannoukos, I; Anagnostopoulos, C-N; Loumos, V; Kayafas, E

dc.contributor.author	Giannoukos, I	en
dc.contributor.author	Anagnostopoulos, C-N	en
dc.contributor.author	Loumos, V	en
dc.contributor.author	Kayafas, E	en
dc.date.accessioned	2014-03-01T01:34:02Z
dc.date.available	2014-03-01T01:34:02Z
dc.date.issued	2010	en
dc.identifier.issn	0031-3203	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20656
dc.subject	High resolution image processing	en
dc.subject	License Plate Recognition	en
dc.subject	Operator Context Scanning algorithm	en
dc.subject	Sliding windows analysis	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Amount of information	en
dc.subject.other	Computational costs	en
dc.subject.other	Data sets	en
dc.subject.other	Environmental conditions	en
dc.subject.other	High definition video	en
dc.subject.other	High-resolution image processing	en
dc.subject.other	Image-based	en
dc.subject.other	Input image	en
dc.subject.other	Intelligent image processing	en
dc.subject.other	License plate detection	en
dc.subject.other	License plate recognition	en
dc.subject.other	License plate recognition systems	en
dc.subject.other	Novel algorithm	en
dc.subject.other	Operator Context Scanning algorithm	en
dc.subject.other	Real-time license plate recognition	en
dc.subject.other	Regions of interest	en
dc.subject.other	Scanning input	en
dc.subject.other	Sliding Window	en
dc.subject.other	Sliding-window analysis	en
dc.subject.other	System effectiveness	en
dc.subject.other	System's performance	en
dc.subject.other	Algorithms	en
dc.subject.other	Decoding	en
dc.subject.other	Imaging systems	en
dc.subject.other	License plates (automobile)	en
dc.subject.other	Mathematical operators	en
dc.subject.other	Object recognition	en
dc.subject.other	Scanning	en
dc.subject.other	Optical character recognition	en
dc.title	Operator context scanning to support high segmentation rates for real time license plate recognition	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.patcog.2010.06.008	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.patcog.2010.06.008	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	Introducing high definition videos and images in object recognition has provided new possibilities in the field of intelligent image processing and pattern recognition. However, due to the large amount of information that needs to be processed, the computational costs are high, making the HD systems slow. To this end, a novel algorithm applied to sliding window analysis, namely Operator Context Scanning (OCS), is proposed and tested on the license plate detection module of a License Plate Recognition (LPR) system. In the LPR system, the OCS algorithm is applied on the Sliding Concentric Windows pixel operator and has been found to improve the LPR system's performance in terms of speed by rapidly scanning input images focusing only on regions of interest, while at the same time it does not reduce the system effectiveness. Additionally, a novel characteristic is presented, namely, the context of the image based on a sliding windows operator. This characteristic helps to quickly categorize the environmental conditions upon which the input image was taken. The algorithm is tested on a data set that includes images of various resolutions, acquired under a variety of environmental conditions. (C) 2010 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Pattern Recognition	en
dc.identifier.doi	10.1016/j.patcog.2010.06.008	en
dc.identifier.isi	ISI:000280983100015	en
dc.identifier.volume	43	en
dc.identifier.issue	11	en
dc.identifier.spage	3866	en
dc.identifier.epage	3878	en