Mesh Modeling for Sparse Image Data Sets

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE Computer Society
Pages1342-1345
Number of pages4
DOIs
Publication statusPublished - Sep 2005
EventIEEE International Conference on Image Processing (ICIP 2005) - Genoa, Italy
Duration: 1 Sep 2005 → …

Conference

ConferenceIEEE International Conference on Image Processing (ICIP 2005)
Period1/09/05 → …

Keywords

  • mesh modelling
  • sparse image data
  • feature extraction

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  • Cite this

    Coleman, SA., & Scotney, BW. (2005). Mesh Modeling for Sparse Image Data Sets. In Unknown Host Publication (pp. 1342-1345). IEEE Computer Society. https://doi.org/10.1109/ICIP.2005.1530312