Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation
- Submitting institution
-
Edge Hill University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20350781
- Type
- D - Journal article
- DOI
-
10.1109/TVCG.2015.2410272
- Title of journal
- IEEE Transactions on Visualization and Computer Graphics
- Article number
- -
- First page
- 1058
- Volume
- 21
- Issue
- 9
- ISSN
- 1077-2626
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- 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
-
4
- Research group(s)
-
-
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The proposed adaptive boosting method has attracted 16 citations since its publication in 2015. While it is elegant in handling the large proportion (up to 95%) of outliers, inevitably introduced through the feature extraction and matching (FEM), it has a particular practical value with computational efficiency for real world applications such as object modelling, robot autonomous navigation and industrial quality assurance. It has opened a novel avenue to tackle the challenging FEM problem: instead of improving FEM methods, it is possible to focus on the evaluation of the established correspondences and thus influence its perception, planning, implementation and resources.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -