On variational methods for fluid flow estimation

Authors:
Paul Ruhnau;Jing Yuan;Christoph Schnörr
Affiliations:
Department of Mathematics and Computer Science, Computer Vision, Graphics, and Pattern Recognition Group, University of Mannheim, Mannheim, Germany;Department of Mathematics and Computer Science, Computer Vision, Graphics, and Pattern Recognition Group, University of Mannheim, Mannheim, Germany;Department of Mathematics and Computer Science, Computer Vision, Graphics, and Pattern Recognition Group, University of Mannheim, Mannheim, Germany
Venue:
IWCM'04 Proceedings of the 1st international conference on Complex motion
Year:
2004

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Abstract

We present global variational approaches that are capable of extracting high-resolution velocity vector fields from image sequences of fluids. Starting points are existing variational approaches from image processing that we adapt to the requiremements of particle image sequences, paying particular attention to a multiscale representation of the image data. Additionally, we combine a discrete non-differentiable particle matching term with a continuous regularization term and thus achieve a variational particle tracking approach. As higher-order regularization can be used to preserve important flow structures, we finally sketch a motion estimation scheme based on the decomposition of motion vector fields into components of orthogonal subspaces.