Performance of optical flow techniques
International Journal of Computer Vision
Robust computation of optical flow in a multi-scale differential framework
International Journal of Computer Vision
Tikhonov Regularization and Total Least Squares
SIAM Journal on Matrix Analysis and Applications
Rationalising the Renormalisation Method of Kanatani
Journal of Mathematical Imaging and Vision
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
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)
Fast and Accurate Motion Estimation Using Orientation Tensors and Parametric Motion Models
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
Accurate dense optical flow estimation using adaptive structure tensors and a parametric model
IEEE Transactions on Image Processing
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
Over-Parameterized Variational Optical Flow
International Journal of Computer Vision
Range Flow Estimation based on Photonic Mixing Device Data
International Journal of Intelligent Systems Technologies and Applications
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In this paper we introduce a principled approach to modeling the image brightness constraint for optical flow algorithms. Using a simple noise model, we derive a probabilistic representation for optical flow. This representation subsumes existing approaches to flow modeling, provides insights into the behaviour and limitations of existing methods and leads to modified algorithms that outperform other approaches that use the brightness constraint. Based on this representation we develop algorithms for flow estimation using different smoothness assumptions, namely constant and affine flow. Experiments on standard data sets demonstrate the superiority of our approach.