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Image Feature Detection on Content-Based Meshes

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

Coleman, SA, Scotney, BW and Herron, MG (2002) Image Feature Detection on Content-Based Meshes. In: IEEE International Conference on Image Processing (ICIP 2002), Rochester, New York. IEEE Signal Processing Society. Vol 1 4 pp. [Conference contribution]

Full text not available from this repository.

DOI: 10.1109/ICIP.2002.1038157

Abstract

Non-uniformly sampled images represented on irregular content-based meshes are central to the developments in image compression techniques and in efficient motion tracking. We present a general approach to the development of systematic design procedures for scalable and adaptive low level image processing operators that can be applied to such non-uniformly sampled images. We provide algorithms that use the content-based mesh to address the usually difficult issue of local operator scale selection. The operator scale is therefore automatically matched to the local scale of the image features as embodied in the mesh. In this way we are able to apply a range of operators directly to compressed images. We demonstrate the approach with the design of image derivative operators that enable image feature detection to be implemented directly on compressed images.

Item Type:Conference contribution (Paper)
Keywords:Fetaure detection, content-based meshes, non-uniformly sampled images, image derivative operators, adaptive image operators
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
Faculty of Computing & Engineering > School of Computing and Mathematics
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:6784
Deposited By:Professor Bryan Scotney
Deposited On:23 May 2011 15:37
Last Modified:23 May 2011 15:37

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