Edge Detection on Hexagonal Structure

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dc.contributor.author Wu, Qiang en_US
dc.contributor.author Jia, Wenjing en_US
dc.contributor.author Hintz, Thomas en_US
dc.contributor.author He, Sean en_US
dc.contributor.editor en_US
dc.date.accessioned 2010-05-28T09:46:53Z
dc.date.available 2010-05-28T09:46:53Z
dc.date.issued 2008 en_US
dc.identifier 2008001682 en_US
dc.identifier.citation He Xiangjian et al. 2008, 'Edge Detection on Hexagonal Structure', Multi-Science Publishing Co. Ltd, vol. 2, no. 1, pp. 61-78. en_US
dc.identifier.issn 1748-3018 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/9001
dc.description.abstract Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that in the human vision system the edge points always appear where the grey-level value is greatly changed. Spiral Architecture is a relatively new image data structure that is inspired from anatomical considerations of the primate's vision. In Spiral Architecture, each image is represented as a collection of hexagonal pixels. Edge detection on Spiral Architecture has features of fast computation and accurate localization. In this paper, we present and compare gradient-based edge detection algorithms on Spiral Architecture. The experimental results show that the edge detection on Spiral Architecture outperforms that on traditional square image structure. en_US
dc.language en_US
dc.publisher Multi-Science Publishing Co. Ltd en_US
dc.relation.hasversion Accepted manuscript version
dc.title Edge Detection on Hexagonal Structure en_US
dc.parent Journal of Algorithms & Computational Technoiogy en_US
dc.journal.volume 2 en_US
dc.journal.number 1 en_US
dc.publocation Essex, UK en_US
dc.identifier.startpage 61 en_US
dc.identifier.endpage 78 en_US
dc.cauo.name INEXT Research Strength Core en_US
dc.conference Verified OK en_US
dc.for 080200 en_US
dc.personcode 990421 en_US
dc.personcode 000748 en_US
dc.personcode 044299 en_US
dc.personcode 830145 en_US
dc.percentage 100 en_US
dc.classification.name Computation Theory and Mathematics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.staffid en_US
dc.staffid 830145 en_US

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