The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Duality TV-L1 flow with fundamental matrix prior
Abstract
Variational techniques yield the most accurate results for dense optical flow fields between two images. They have the nice property of inherent smoothness to cope with untextured image regions: the filling-in of such regions is driven by neighbouring pixels. Such filling-in is not always the best choice. If the scene is mostly stationary and the camera is moving, the direction of the optical flow vectors can be restricted using the fundamental matrix. In this paper we propose an exact solution of the variational optical flow, using the fundamental matrix geometry as an additional weak prior. Our novel approach currently performs best on the Middlebury flow evaluation which includes images from stationary and dynamic scenes.
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F-TV-L1 uses a rigidity prior.
“F-TV-L1 uses a rigidity prior”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Wedel et al. 2008
One algorithm that uses rigidity (F-TV-L1-Wedel et al. 2008) does poorly on the non-rigid scenes.
“one algorithm that uses rigidity (F-TV-L1-Wedel et al. 2008) does poorly on the non-rigid scenes”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Wedel et al. 2008
F-TV-L1 (Wedel et al. 2008) does poorly on the non-rigid sequences (Army, Mequon, Schefflera, and Wooden).
“but poorly on the non-rigid sequences ( Army , Mequon , Schefflera , and Wooden ).”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Wedel et al. 2008
F-TV-L1 (Wedel et al. 2008) does well on the largely rigid sequences (Grove, Urban, Yosemite, and Teddy).
“F-TV-L1 (Wedel et al. 2008) does well on the largely rigid sequences ( Grove , Urban , Yosemite , and Teddy )”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Wedel et al. 2008
The rigidity prior component of F-TV-L1 seems to be used too aggressively.
“so it seems that this component is being used too aggressively.”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Wedel et al. 2008
Citations
Cites
A Duality Based Approach for Realtime TV-L 1 Optical Flow
Zach, C., Pock, T., & Bischof, H. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74936-3_22 · Cited by 1,658 · cited 6×
“The main contribution of [5] is the reformulation of the original variational energy functional into a convex dual form, allowing splitting between the data term and the smoothness term in an iterative solving process.”
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Research works printed but not linked: 13
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- horn, 1980, Determining optical flow
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- valgaerts, 2008, a variational approach for the joint recovery of the optical flow and the fundamental matrix, Pattern Recognition (Proc DAGM), 10.1007/978-3-540-69321-5_32 · DOI
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Cited by
Sorted by citations
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
Metadata
- Authors
- Wedel, A., Pock, T., Braun, J., Franke, U., & Cremers, D.
- Affiliations
- University of Bonn (DE)
- Daimler (Germany) (DE)
- Venue
- 2008 23rd International Conference Image and Vision Computing New Zealand
- Publisher
- IEEE
- Published
- 2008-11
- Pages
- 1-6
- OpenAlex
- W2109432061
- Cited by
- 58
- References
- 1
- Citations by year
- 2025: 1 · 2022: 2 · 2021: 3 · 2020: 4 · 2019: 2 · 2018: 2 · 2016: 3 · 2015: 5 · 2014: 9 · 2013: 5 · 2012: 3
- FWCI
- 4.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Image and Vision Computing New Zealand (IVCNZ) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Stabilization
- Keywords
- dense optical flow, variational optical flow, fundamental matrix, camera movement
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