Wavelet Expansion and High-order Regularization for Multiscale Fluid-motion Estimation

Source: OAI


We consider a novel optic flow estimation algorithm based on a wavelet expansion of the velocity field. In particular, we propose an efficient gradient-based estimation algorithm which naturally encompasses the estimation process into a multiresolution framework while avoiding most of the drawbacks common to this kind of hierarchical methods. We then emphasize that the proposed methodology is well-suited to the practical implementation of high-order regularizations. The powerfulness of the proposed algorithm and regularization schemes are finally assessed by simulation results on challenging image sequence of turbulent fluids.

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Available from: Cedric Herzet, Oct 07, 2015
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  • Publicacions Matematiques 01/1991; 35. DOI:10.5565/PUBLMAT_35191_06 · 0.46 Impact Factor
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    ABSTRACT: An image registration algorithm is developed to estimate dense motion vectors between two images using the coarse-to-fine wavelet-based motion model. This motion model is described by a linear combination of hierarchical basis functions proposed by Cai and Wang (SIAM Numer. Anal., 33(3):937–970, 1996). The coarser-scale basis function has larger support while the finer-scale basis function has smaller support. With these variable supports in full resolution, the basis functions serve as large-to-small windows so that the global and local information can be incorporated concurrently for image matching, especially for recovering motion vectors containing large displacements. To evaluate the accuracy of the wavelet-based method, two sets of test images were experimented using both the wavelet-based method and a leading pyramid spline-based method by Szeliski et al. (International Journal of Computer Vision, 22(3):199–218, 1996). One set of test images, taken from Barron et al. (International Journal of Computer Vision, 12:43–77, 1994), contains small displacements. The other set exhibits low texture or spatial aliasing after image blurring and contains large displacements. The experimental results showed that our wavelet-based method produced better motion estimates with error distributions having a smaller mean and smaller standard deviation.
    International Journal of Computer Vision 01/2000; 38:129-152. DOI:10.1023/A:1008101718719 · 3.81 Impact Factor