A fully convolutional two-stream fusion network for interactive image segmentation
- Submitting institution
-
Loughborough University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1960
- Type
- D - Journal article
- DOI
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10.1016/j.neunet.2018.10.009
- Title of journal
- Neural Networks
- Article number
- -
- First page
- 31
- Volume
- 109
- Issue
- -
- ISSN
- 0893-6080
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- 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
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research outperforms the state-of-the-art in interactive image segmentation, a highly competitive field that has seen considerable improvements over the last five years, mainly driven by leading research groups and large companies. Developed in collaboration with the industry (KTP project nr: 10412 this paper has been built on by global research labs such as Google Research and Adobe Research , and by a patent (CN 110211146 A).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -