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A Rotation-Invariant Regularization Term for Optical Flow Related Problems

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9007)


This paper proposes a new regularization term for optical flow related problems. The proposed regularizer properly handles rotation movements and it also produces good smoothness conditions on the flow field while preserving discontinuities. We also present a dual formulation of the new term that turns the minimization problem into a saddle-point problem that can be solved using a primal-dual algorithm. The performance of the new regularizer has been compared against the Total Variation (TV) in three different problems: optical flow estimation, optical flow inpainting, and optical flow completion from sparse samples. In the three situations the new regularizer improves the results obtained with the TV as a smoothing term.


  • Optical Flow
  • Proposed Regularization Term
  • Sparse Sampling
  • Motion Inpainting
  • Ground Truth Optical Flow

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  • DOI: 10.1007/978-3-319-16814-2_20
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We acknowledge partial support by MICINN project, reference MTM2012-30772, by GRC reference 2009 SGR 773 funded by the Generalitat de Catalunya, and by the ERC Advanced Grant INPAINTING (Grant agreement no.: 319899). The second author acknowledges partial support to the Ramón y Cajal program of the MINECO.

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Correspondence to Roberto P. Palomares .

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Palomares, R.P., Haro, G., Ballester, C. (2015). A Rotation-Invariant Regularization Term for Optical Flow Related Problems. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham.

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