Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and 
Layers

Wulff, Jonas; Black, Michael J.

doi:10.1109/CVPR.2015.7298607

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Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers

Wulff, J., & Black, M. J. (2015). Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) (pp. 120-130). Piscataway, NJ: IEEE. doi:10.1109/CVPR.2015.7298607.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002A-1F32-1 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-B22A-E

Genre: Conference Paper

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Creators:
Wulff, Jonas¹, Author
Black, Michael J.¹, Author

Affiliations:
1Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497642

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Free keywords: Abt. Black

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Language(s): eng - English

Dates: Published Online: 2015-10-15Date issued: 2015

Publication Status: Issued

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Identifiers: BibTex Citekey: Wulff:CVPR:2015
DOI: 10.1109/CVPR.2015.7298607

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Title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015)

Place of Event: Boston, MA

Start-/End Date: 2015-06-07 - 2015-06-12

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Title: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015)

Source Genre: Proceedings

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Publ. Info: Piscataway, NJ : IEEE

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 120 - 130 Identifier: ISBN: 978-1-4673-6964-0
ISBN: 978-1-4673-6963-3
ISBN: 978-1-4673-6965-7
ISSN: 1063-6919
DOI: 10.1109/CVPR31182.2015