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An Evaluation of Mesh Model Algorithms For Direct Feature Detection on Compressed Image Representations

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

Scotney, BW, Coleman, SA and Herron, MG (2003) An Evaluation of Mesh Model Algorithms For Direct Feature Detection on Compressed Image Representations. In: IEEE International Conference on Image Processing (ICIP 2003), Barcelona, Spain. IEEE Signal Processing Society. Vol 1 4 pp. [Conference contribution]

Full text not available from this repository.

DOI: 10.1109/ICIP.2003.1247054

Abstract

Recent developments in mesh modelling of images have provided algorithms that can achieve accurate and efficient image representations without the high computational cost associated with earlier optimisation-based methods. Hence nonuniform sampling of images combined with the use of irregular content-based meshing has provided a successful basis for recent developments in image compression techniques. The evaluation of these techniques has focussed on the accuracy and efficiency with which the mesh model can represent the image. For real-time applications, the usefulness of a mesh model may be assessed by its ability to yield compressed image representations that can be processed directly to provide output that is sufficiently accurate. Hence we present an evaluation of mesh model algorithms that is based on feature detection on the associated compressed image representations. Such an approach is built on the recent development of systematic design procedures for scalable and adaptive image processing operators that can be applied directly to non-uniformly sampled images. We demonstrate the approach using image derivative operators on compressed images.

Item Type:Conference contribution (Paper)
Keywords:mesh modelling, content-based meshes, compressed image representation, feature extraction, performance evaluation, image derivative operators, non-uniform image sampling
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:6800
Deposited By:Professor Bryan Scotney
Deposited On:23 May 2011 15:39
Last Modified:23 May 2011 15:39

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