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Mesh Modeling for Sparse Image Data Sets

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

Coleman, SA and Scotney, BW (2005) Mesh Modeling for Sparse Image Data Sets. In: IEEE International Conference on Image Processing (ICIP 2005), Genoa, Italy. IEEE Computer Society. 4 pp. [Conference contribution]

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DOI: 10.1109/ICIP.2005.1530312

Abstract

Incomplete image data sets are of interest in many domains and arise in a variety of applications, and in particular in applications that use remote sensor array data. Although 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, such techniques rely on the availability of the entire image data. These content-based mesh modelling techniques aim to provide a high sample density in regions of interest, such as feature neighbourhoods or around moving objects, whilst achieving efficiency by retaining a low overall image sampling density. The sampling density is determined by a feature map, such as local image curvature or local spatial-frequency content that is obtained from the underlying complete image data. As the requirement for the availability of complete image data makes such content-based mesh modelling techniques unsuitable for application to incomplete images, where an image consists of a sparse data set, we aim to address this issue by proposing an alternative approach to mesh modelling that is based on automatically adaptive feature detection directly applicable to sparsely sampled images.

Item Type:Conference contribution (Paper)
Keywords:mesh modelling, sparse image data, feature extraction
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:6820
Deposited By:Professor Bryan Scotney
Deposited On:23 May 2011 15:40
Last Modified:23 May 2011 15:40

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