Abstract
| Language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| Pages | 1342-1345 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - Sep 2005 |
| Event | IEEE International Conference on Image Processing (ICIP 2005) - Genoa, Italy Duration: 1 Sep 2005 → … |
Conference
| Conference | IEEE International Conference on Image Processing (ICIP 2005) |
|---|---|
| Period | 1/09/05 → … |
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Keywords
- mesh modelling
- sparse image data
- feature extraction
Cite this
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Mesh Modeling for Sparse Image Data Sets. / Coleman, SA; Scotney, BW.
Unknown Host Publication. 2005. p. 1342-1345.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Mesh Modeling for Sparse Image Data Sets
AU - Coleman, SA
AU - Scotney, BW
PY - 2005/9
Y1 - 2005/9
N2 - 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.
AB - 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.
KW - mesh modelling
KW - sparse image data
KW - feature extraction
U2 - 10.1109/ICIP.2005.1530312
DO - 10.1109/ICIP.2005.1530312
M3 - Conference contribution
SP - 1342
EP - 1345
BT - Unknown Host Publication
ER -