IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection
International Journal of Computer Vision
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
On the Spatial Statistics of Optical Flow
International Journal of Computer Vision
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach
International Journal of Computer Vision
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Computer Vision and Image Understanding
International Journal of Computer Vision
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Dense optical flow estimation in images is a challenging problem because the algorithm must coordinate the estimated motion across large regions in the image, while avoiding inappropriate smoothing over motion boundaries. Recent works have advocated for the use of nonlocal regularization to model long-range correlations in the flow. However, incorporating nonlocal regularization into an energy optimization framework is challenging due to the large number of pairwise penalty terms. Existing techniques either substitute intermediate filtering of the flow field for direct optimization of the nonlocal objective, or suffer substantial performance penalties when the range of the regularizer increases. In this paper, we describe an optimization algorithm that efficiently handles a general type of nonlocal regularization objectives for optical flow estimation. The computational complexity of the algorithm is independent of the range of the regularizer. We show that nonlocal regularization improves estimation accuracy at longer ranges than previously reported, and is complementary to intermediate filtering of the flow field. Our algorithm is simple and is compatible with many optical flow models.