Mesh Modeling for Sparse Image Data Sets

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


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
Number of pages4
Publication statusPublished (in print/issue) - Sept 2005
EventIEEE International Conference on Image Processing (ICIP 2005) - Genoa, Italy
Duration: 1 Sept 2005 → …


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


  • mesh modelling
  • sparse image data
  • feature extraction


Dive into the research topics of 'Mesh Modeling for Sparse Image Data Sets'. Together they form a unique fingerprint.

Cite this