An extension to the brightness clustering transform and locally contrasting keypoints
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 34136246
- Type
- D - Journal article
- DOI
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10.1007/s00138-016-0785-3
- Title of journal
- Machine Vision and Applications
- Article number
- -
- First page
- 1187
- Volume
- 27
- Issue
- 8
- ISSN
- 0932-8092
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2016
- URL
-
-
- Supplementary information
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- 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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1
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Feature extraction is intrinsic to understanding images. This approach is the first region-based approach to interest point detection and has performance advantages over contemporaneous techniques. Formulation using regions can accrue smoothing in the feature space, allowing robust feature extraction. The paper was invited from those presented at CAIP 2015 where it won the Best Paper prize http://caip.eu.org/caip2015/awards/ . The work features in the 4th Edition of Nixon’s major textbook on Feature Extraction https://www.amazon.com/Feature-Extraction-Processing-Computer-Vision/dp/0128149760/ref=dp_ob_title_bk#reader_0128149760 (pp194-197).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -