Ulster University Logo

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

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 Mathematics
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Faculty of Computing & Engineering > School of Computing and Information Engineering
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:6800
Deposited By: Professor Bryan Scotney
Deposited On:23 May 2011 14:39
Last Modified:23 May 2011 14:39

Repository Staff Only: item control page