A Unified Deep Metric Representation for Mesh Saliency Detection and Non-rigid Shape Matching
- Submitting institution
-
University of Durham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 127257
- Type
- D - Journal article
- DOI
-
10.1109/TMM.2019.2952983
- Title of journal
- IEEE Transactions on Multimedia
- Article number
- -
- First page
- 2278
- Volume
- 22
- Issue
- 9
- ISSN
- 15209210
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
https://doi.org/10.1109/TMM.2019.2952983
- 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
-
4
- Research group(s)
-
A - Innovative Computing
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is the first to generate a unified metric through deep learning to model mesh saliency and shape matching jointly, mutually enhancing the accuracy of both measurements, while existing research has only addressed the two problems separately.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -