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Efficient relaxations for dense CRFs with sparse higher-order potentials

Abstract:

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 counterparts. Currently, the state-of-the-art algorithm performs mean-field inference using a filter-based method but fails to provide a strong theoretical guarantee on the quality of...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1137/18M1178104

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-6431-0775
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
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Publisher:
Society for Industrial and Applied Mathematics Publisher's website
Journal:
SIAM Journal on Imaging Sciences Journal website
Publication date:
2019-01-30
Acceptance date:
2018-11-08
DOI:
EISSN:
1936-4954
Source identifiers:
895698
Keywords:
Pubs id:
pubs:895698
UUID:
uuid:ccdd914f-013e-4596-9bb7-062eed7dfc7c
Local pid:
pubs:895698
Deposit date:
2018-11-16

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