Quantized Census for Stereoscopic Image Matching
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 747
- Type
- E - Conference contribution
- DOI
-
10.1109/3DV.2014.83
- Title of conference / published proceedings
- 2014 2nd International Conference on 3D Vision
- First page
- 22
- Volume
- -
- Issue
- -
- ISSN
- 1550-6185
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- 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
-
3
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The output presents a robust method that improves computer vision-based depth capture under specific conditions, while being cheap and efficient to calculate. The technique is a significant improvement over the routinely used Census technique. We have shown it to be particularly valuable in hand pose-recognition in follow-on research collaborations with industry partner Huawei Technologies Research & Development (Basaru et al 2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
