Conference item
Learning covariant feature detectors
- Abstract:
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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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- Files:
-
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(Accepted manuscript, pdf, 2.0MB)
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- Publisher copy:
- 10.1007/978-3-319-49409-8_11
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Authors
Bibliographic Details
- 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:
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0302-9743
- Source identifiers:
-
656431
- ISBN:
- 9783319494081
Item Description
- Pubs id:
-
pubs:656431
- UUID:
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uuid:37b8b819-83a9-4716-9e7d-68f73150ea9c
- Local pid:
- pubs:656431
- Deposit date:
- 2016-11-01
Terms of use
- Copyright holder:
- Springer International Publishing
- Copyright date:
- 2016
- Notes:
- Copyright © 2016 Springer International Publishing Switzerland. This is the accepted manuscript version of the article. The final version is available online from Springer at: https://doi.org/10.1007/978-3-319-49409-8_11
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