IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
Relations Between Regularization and Diffusion Filtering
Journal of Mathematical Imaging and Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Gaussian Scale-Space Theory
Diffusions and Confusions in Signal and Image Processing
Journal of Mathematical Imaging and Vision
On Functionals with Greyvalue-Controlled Smoothness Terms for Determining Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of Optimal Stopping Time for Nonlinear Diffusion Filtering
International Journal of Computer Vision
A Geometric Framework and a New Criterion in Optical Flow Modeling
Journal of Mathematical Imaging and Vision
On a Decomposition Model for Optical Flow
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
From box filtering to fast explicit diffusion
Proceedings of the 32nd DAGM conference on Pattern recognition
International Journal of Computer Vision
A morphological, affine, and Galilean invariant scale-space for movies
IEEE Transactions on Image Processing
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While image scale spaces are well understood, it is undeniable that the regularisation parameter in variational optic flow methods serves a similar role as the scale parameter in scale space evolutions. However, no thorough analysis of this optic flow scale-space exists to date. Our paper closes this gap by interpreting variational optic flow methods as Whittaker-Tikhonov regularisations of the normal flow, evaluated in a constraint-specific norm. The transition from this regularisation framework to an optic flow evolution creates novel vector-valued scale-spaces that are not in divergence form and act in a highly anisotropic way. From a practical viewpoint, the deep structure in optic flow scale space allows the automatic selection of the most accurate scale by means of an optimal prediction principle. Moreover, we show that our general class of optic flow scale-spaces incorporates novel methods that outperform classical variational approaches.