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Near-Circular Corner and Edge Detection Operators

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Kerr, D, Coleman, SA and Scotney, BW (2007) Near-Circular Corner and Edge Detection Operators. In: Machine Vision and Image Processing Conference (IMVIP 2007), Maynooth, Ireland. IEEE Computer Society. 8 pp. [Conference contribution]

Full text not available from this repository.

DOI: 10.1109/IMVIP.2007.14

Abstract

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence corner detection is often used for this purpose. However, to obtain a higher level description of an image, such as identification of objects, additional information such as edges is required, and more recently detectors have been proposed that find both edges and corners. Recently, finite-element based methods have been used to develop gradient operators for edge detection that have improved angular accuracy over standard techniques. We extend this work to corner detection, enabling edge and corner detection to be integrated. In addition we present a combined operator, enabling edge and corner detection to be achieved concurrently, and we demonstrate that accuracy is comparable to well- known existing corner detectors and edge detectors, and, as standard post-smoothing of the corner map is not required, significantly reduced computation time can be achieved.

Item Type:Conference contribution (Paper)
Keywords:edge detection , finite element analysis, gradient operator , near-circular operator
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:6837
Deposited By:Professor Bryan Scotney
Deposited On:23 May 2011 15:43
Last Modified:07 Nov 2011 16:54

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