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dc.contributor.author Won, Woong-Jae ko
dc.contributor.author Lee, Minho ko
dc.contributor.author Son, Joon-Woo ko
dc.date.available 2017-07-11T08:24:18Z -
dc.date.created 2017-05-08 -
dc.date.issued 2008 -
dc.identifier.citation 2008 IEEE Intelligent Vehicles Symposium, IV, pp.542 - 547 -
dc.identifier.isbn 9780000000000 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/3981 -
dc.description.abstract In this paper, we proposed a new road traffic sign detection model based on human-like selective attention mechanism for implementing interactive workload manager system. Since the road traffic sign boards have dominant color contrast against backgrounds, we consider the color opponents and its edge information with center surround difference and normalization as a pre-processing, which is effective to intensify the sign board color characteristics as well as reduce background noise influence. After constructing the road traffic sign saliency map using the edge and color feature maps, the candidate road traffic sign regions are selected by local maximum energy searching with entropy maximization algorithm to find suitable size of the sign board areas. Computational experiment results show that the proposed model can successfully detect a road traffic sign board. © 2008 IEEE. -
dc.publisher Institute of Electrical and Electronics Engineers Inc. -
dc.title Implementation of road traffic signs detection based on saliency map model -
dc.type Conference -
dc.identifier.doi 10.1109/IVS.2008.4621144 -
dc.identifier.scopusid 2-s2.0-57749199877 -
dc.type.rims CONF -
dc.contributor.nonIdAuthor Lee, Minho -
dc.identifier.citationStartPage 542 -
dc.identifier.citationEndPage 547 -
dc.identifier.citationTitle 2008 IEEE Intelligent Vehicles Symposium, IV -
dc.type.journalArticle Conference Paper -
dc.identifier.conferencecountry US -
dc.identifier.conferencelocation Eindhoven -
dc.contributor.affiliatedAuthor Won, Woong-Jae -
dc.contributor.affiliatedAuthor Son, Joon-Woo -
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Companion Diagnostics and Medical Technology Research Group 2. Conference Papers

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