A generalisable framework for saliency-based line segment detection
- Submitting institution
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The University of Surrey
- Unit of assessment
- 12 - Engineering
- Output identifier
- 9006741_2
- Type
- D - Journal article
- DOI
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10.1016/j.patcog.2015.06.015
- Title of journal
- Pattern Recognition
- Article number
- -
- First page
- 3993
- Volume
- 48
- Issue
- 12
- ISSN
- 0031-3203
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
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- Supplementary information
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- Request cross-referral to
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- 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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- Research group(s)
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- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes the first approach for multi-modal salient line detection. The approach has been extensively evaluated against state-of-the-art line detectors on several publicly available benchmark datasets as well as new large scale multimodal datasets publicly released to the scientific community. This research has been evaluated in the context of digital film production as part of EC FP7 project IMPART (grant #13968) and was integrated in production tools for 2D/3D multimodal data indexing by industry partners (Double Negative, Filmlight). This enabled the development of the first globally optimal correspondence-free 2D-3D registration method published at the premier computer vision conference ICCV2015.
- Author contribution statement
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- Non-English
- No
- English abstract
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