- Abstract:
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Dense conditional random fields (CRFs) have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segmentation. By modelling long- range interactions, dense CRFs provide a labelling that captures finer detail than their sparse cou...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's Version
- Files:
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(pdf, 1.5mb)
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- Publisher copy:
- 10.1137/18M1178104
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- Publisher:
- Society for Industrial and Applied Mathematics Publisher's website
- Journal:
- SIAM Journal on Imaging Sciences Journal website
- Acceptance date:
- 2018-11-08
- DOI:
- EISSN:
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1936-4954
- Pubs id:
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pubs:895698
- URN:
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uri:ccdd914f-013e-4596-9bb7-062eed7dfc7c
- UUID:
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uuid:ccdd914f-013e-4596-9bb7-062eed7dfc7c
- Local pid:
- pubs:895698
- Keywords:
- Copyright holder:
- Society for Industrial and Applied Mathematics
- Notes:
- © 2019, Society for Industrial and Applied Mathematics. This is the publishers version of the article. The final version is available online from Society for Industrial and Applied Mathematics at: https://doi.org/10.1137/18M1178104
Journal article
Efficient relaxations for dense CRFs with sparse higher-order potentials
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