The existence of geometrical density—image transformations corresponding to object motion
Computer Vision, Graphics, and Image Processing
Computing occluding and transparent motions
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
International Journal of Computer Vision
Robustly estimating changes in image appearance
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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)
Spatio-temporal filters for transparent motion segmentation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Divide-and-conquer strategies for estimating multiple transparent motions
IWCM'04 Proceedings of the 1st international conference on Complex motion
Entropy controlled gauss-markov random measure field models for early vision
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
A theory of multiple orientation estimation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Variational Multi-Valued Velocity Field Estimation for Transparent Sequences
Journal of Mathematical Imaging and Vision
Multi-layer deformation estimation for fluoroscopic imaging
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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We propose a variational approach for multi-valued velocity field estimation in transparent sequences. Starting from existing local motion estimators, we show a variational model for integrating in space and time these local estimations to obtain a robust estimation of the multi-valued velocity field. With this approach, we can indeed estimate some multi-valued velocity fields which are not necessarily piecewise constant on a layer: Each layer can evolve according to non-parametric optical flow. We show how our approach outperforms some existing approaches, and we illustrate its capabilities on several challenging synthetic/ real sequences.