Cluster-based point set saliency
- Submitting institution
-
University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1886
- Type
- E - Conference contribution
- DOI
-
10.1109/ICCV.2015.27
- Title of conference / published proceedings
- Proceedings of the IEEE International Conference on Computer Vision
- First page
- 163
- Volume
- 2015
- Issue
- -
- ISSN
- 1550-5499
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 19
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper deals with the challenge of saliency detection in 3D objects. The originality lies in detecting saliency in a point cloud, where there is no topological information to determine the surface of the object. Traditional saliency detection depends heavily on topology. We demonstrate that it is possible to produce a good saliency metric using just the point cloud information. This allows saliency-based methods to be extended to the large and increasing number of 3D data sets captured as point clouds by, for example, laser scanners.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
