Deterministic Approximate Methods for Maximum Consensus Robust Fitting
- Submitting institution
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The University of Liverpool
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 15838
- Type
- D - Journal article
- DOI
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10.1109/tpami.2019.2939307
- Title of journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Article number
- -
- First page
- 1
- Volume
- 43
- Issue
- 3
- ISSN
- 0162-8828
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- 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
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4
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article was the first to propose a deterministic algorithm to solve the maximum consensus estimation problem. While only very recently published, the paper has already influenced further work such as "Efficient Algorithms for Maximum Consensus Robust Fitting" (IEEE Trans. Robotics, 2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -