A scalar function formulation for optical flow
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Dense Estimation of Fluid Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Physically based fluid flow recovery from image sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Variational Assimilation of Fluid Motion from Image Sequence
SIAM Journal on Imaging Sciences
Estimating Apparent Motion on Satellite Acquisitions with a Physical Dynamic Model
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Solving ill-posed Image Processing problems using Data Assimilation
Numerical Algorithms
Optical flow computation using extended constraints
IEEE Transactions on Image Processing
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This paper describes an innovative approach to estimate motion from image observations of divergence-free flows. Unlike most state-of-the-art methods, which only minimize the divergence of the motion field, our approach utilizes the vorticity-velocity formalism in order to construct a motion field in the subspace of divergence free functions. A 4DVAR-like image assimilation method is used to generate an estimate of the vorticity field given image observations. Given that vorticity estimate, the motion is obtained solving the Poisson equation. Results are illustrated on synthetic image observations and compared to those obtained with state-of-the-art methods, in order to quantify the improvements brought by the presented approach. The method is then applied to ocean satellite data to demonstrate its performance on the real images.