Persistent homology to analyse 3D faces and assess body weight gain
- Submitting institution
-
University of Central Lancashire
- Unit of assessment
- 12 - Engineering
- Output identifier
- 16869
- Type
- D - Journal article
- DOI
-
10.1007/s00371-016-1344-7
- Title of journal
- The Visual Computer
- Article number
- -
- First page
- 549
- Volume
- 33
- Issue
- 5
- ISSN
- 0178-2789
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2017
- 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
-
5
- Research group(s)
-
H - Computer Vision and Machine Learning Group
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper reports on one of the outcomes of a major European project, “Semeiotic oriented Technology for Individual’s Cardio-metabolic Risk Self-assessment and Self-monitoring (SEMEOTICONS)”. The €5,383,126 project was funded by the European FP7 programme (Contract no. 611516). The method described, contributed to development of the “Wize Mirror”, a device for assessment of cardio-metabolic risk. The reported research has also led to the organisation of the Visual Computing and Machine Learning for Biomedical Applications (ViMaBi) workshop and a subsequent book published by Springer (https://www.springer.com/gp/book/9783030299293).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
