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CODE: Coherence Based Decision Boundaries for Feature Correspondence

Abstract:

A key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level. This forces adoption of strict similarity thresholds that discard many true matches. However, if analyzed at a global level, false matches are usually randomly scattered while true matches tend to be coherent (clustered around a few dominant motions), thus creating a coherence based separability constraint. This paper proposes a non-linear regression technique tha...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/tpami.2017.2652468

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Role:
Author
ORCID:
0000-0002-5402-5065
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Role:
Author
ORCID:
0000-0001-5550-8758
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Funding agency for:
Torr, P
Grant:
ERC-2012-AdG 321162-HELIOS
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Funding agency for:
Torr, P
Grant:
ERC-2012-AdG 321162-HELIOS
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE Transactions on Pattern Analysis and Machine Intelligence Journal website
Volume:
40
Issue:
1
Pages:
34-47
Publication date:
2017-01-15
Acceptance date:
2016-12-20
DOI:
EISSN:
1939-3539
ISSN:
0162-8828
Pmid:
28092524
Source identifiers:
813641
Language:
English
Keywords:
Pubs id:
pubs:813641
UUID:
uuid:0e5a62ab-fb69-472f-a1e1-49d49595db62
Local pid:
pubs:813641
Deposit date:
2018-01-09

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