IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Computing optical flow via variational techniques
SIAM Journal on Applied Mathematics
Skin and Bones: Multi-layer, Locally Affine, Optical Flow and Regularization with Transparency
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
International Journal of Computer Vision
Simultaneous Higher-Order Optical Flow Estimation and Decomposition
SIAM Journal on Scientific Computing
Over-Parameterized Variational Optical Flow
International Journal of Computer Vision
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
Total absolute Gaussian curvature for stereo prior
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Total-Variation Based Piecewise Affine Regularization
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Optical Flow Computation from an Asynchronised Multiresolution Image Sequence
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Two Step Variational Method for Subpixel Optical Flow Computation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
Shape prior embedded geodesic distance transform for image segmentation
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Improving sub-pixel correspondence through upsampling
Computer Vision and Image Understanding
Over-Parameterized optical flow using a stereoscopic constraint
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
High accuracy TOF and stereo sensor fusion at interactive rates
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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Virtually all variational methods for motion estimation regularize the gradient of the flow field, which introduces a bias towards piecewise constant motions in weakly textured areas. We propose a novel regularization approach, based on decorrelated second-order derivatives, that does not suffer from this shortcoming. We then derive an efficient numerical scheme to solve the new model using projected gradient descent. A comparison to a TV regularized model shows that the proposed second-order prior exhibits superior performance, in particular in low-textured areas (where the prior becomes important). Finally, we show that the proposed model yields state-of-the-art results on the Middlebury optical flow database.