Optimal Orthogonal Basis and Image Assimilation: Motion Modeling

Abstract : This paper describes modeling and numerical computation of orthogonal bases, which are used to describe images and motion fields. Motion estimation from image data is then studied on subspaces spanned by these bases. A reduced model is obtained as the Galerkin projection on these subspaces of a physical model, based on Euler and optical flow equations. A data assimilation method is studied, which assimilates coefficients of image data in the reduced model in order to estimate motion coefficients. The approach is first quantified on synthetic data: it demonstrates the interest of model reduction as a compromise between results quality and computational cost. Results obtained on real data are then displayed so as to illustrate the method.
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Etienne Huot, Isabelle Herlin, Giuseppe Papari. Optimal Orthogonal Basis and Image Assimilation: Motion Modeling. ICCV - International Conference on Computer Vision, Dec 2013, Sydney, Australia. ⟨hal-00871330⟩

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