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Space Variant Feature Extraction for Omni-directional Images

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Kerr, D, Scotney, BW and Coleman, SA (2006) Space Variant Feature Extraction for Omni-directional Images. In: Irish Machine Vision and Image Processing Conference 2006, Dublin City University. Vision System Group, Research Institute for Network and Communications Engineering (RINCE), Faculty of Engineering and Computing, Dublin City University, Dublin 9, Ireland. . 8 pp. [Conference contribution]

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URL: http://elm.eeng.dcu.ie/imvip2006/documents/IMVIP2006Proceedings.pdf

Abstract

In recent years, the use of omni-directional cameras has become increasingly morepopular in vision systems and robotics. To date, most of the research relating to omni-directional cameras has focussed on the design of the camera or the way in which toproject the omni-directional image to a panoramic view rather than on how to processthese images after capture. Typically images obtained from omni-directional cameras are transformed to sparse panoramic images that are interpolated to obtain a complete panoramic view prior to low level image processing. This interpolation presents a significant computational overhead with respect to real-time vision. We present an approach to real-time vision that projects an omni-directional image to a sparse panoramic image and directly processes this sparse image. Feature extraction operators previously designed by the authors are used in this approach but this paper highlights the reduction of the computational overheads of processing images arising from omni-directional camerasthrough efficient coding and storage, whilst retaining accuracy sufficient for application to real-time robot vision.

Item Type:Conference contribution (Paper)
Keywords:Omni-directional imaging, Feature detection
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:20478
Deposited By:Dr Dermot Kerr
Deposited On:15 Nov 2011 10:57
Last Modified:15 Nov 2011 10:57

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