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International Journal of Computer Vision
A scalar function formulation for optical flow
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Dense Estimation of Fluid Flows
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A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion
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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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A Variational Technique for Time Consistent Tracking of Curves and Motion
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On a Decomposition Model for Optical Flow
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On variational methods for fluid flow estimation
IWCM'04 Proceedings of the 1st international conference on Complex motion
A variational framework for spatio-temporal smoothing of fluid motions
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
A fluid motion estimator for schlieren image velocimetry
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
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A study of non-smooth convex flow decomposition
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
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STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
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We present a novel variational approach to dense motion estimation of highly non-rigid structures in image sequences. Our representation of the motion vector field is based on the extended Helmholtz Decomposition into its principal constituents: The laminar flow and two potential functions related to the solenoidal and irrotational flow, respectively. The potential functions, which are of primary interest for flow pattern analysis in numerous application fields like remote sensing or fluid mechanics, are directly estimated from image sequences with a variational approach. We use regularizers with derivatives up to third order to obtain unbiased high-quality solutions. Computationally, the approach is made tractable by means of auxiliary variables. The performance of the approach is demonstrated with ground-truth experiments and real-world data.