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Learning covariant feature detectors

Abstract:

Local covariant feature detection, namely the problem of extracting viewpoint invariant features from images, has so far largely resisted the application of machine learning techniques. In this paper, we propose the first fully general formulation for learning local covariant feature detectors. We propose to cast detection as a regression problem, enabling the use of powerful regressors such as deep neural networks. We then derive a covariance constraint that can be used to automatically lear...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-319-49409-8_11

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
Springer, Cham Publisher's website
Volume:
9915
Pages:
100-117
Series:
Lecture Notes in Computer Science
Host title:
European Conference on Computer Vision: ECCV 2016: Computer Vision – ECCV 2016 Workshops
Publication date:
2016-11-01
Acceptance date:
2016-08-12
Event location:
Amsterdam
DOI:
ISSN:
0302-9743
Source identifiers:
656431
ISBN:
9783319494081
Pubs id:
pubs:656431
UUID:
uuid:37b8b819-83a9-4716-9e7d-68f73150ea9c
Local pid:
pubs:656431
Deposit date:
2016-11-01

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