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Graph Laplacian Regularization for Robust Optical Flow Estimation.
- Source :
-
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society [IEEE Trans Image Process] 2019 Oct 10. Date of Electronic Publication: 2019 Oct 10. - Publication Year :
- 2019
- Publisher :
- Ahead of Print
-
Abstract
- This paper proposes graph Laplacian regularization for robust estimation of optical flow. First, we analyze the spectral properties of dense graph Laplacians and show that dense graphs achieve a better trade-off between preserving flow discontinuities and filtering noise, compared with the usual Laplacian. Using this analysis, we then propose a robust optical flow estimation method based on Gaussian graph Laplacians. We revisit the framework of iteratively reweighted least-squares from the perspective of graph edge reweighting, and employ the Welsch loss function to preserve flow discontinuities and handle occlusions. Our experiments using the Middlebury and MPI-Sintel optical flow datasets demonstrate the robustness and the efficiency of our proposed approach.
Details
- Language :
- English
- ISSN :
- 1941-0042
- Database :
- MEDLINE
- Journal :
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
- Publication Type :
- Academic Journal
- Accession number :
- 31613756
- Full Text :
- https://doi.org/10.1109/TIP.2019.2945653