Optimal orthogonal basis and image assimilation: motion modeling. ICCV

This paper describes modeling and numerical computa-tion of orthogonal bases, which are used to describe im-ages 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 stud-ied, which assimilates coefficients of image data in the re-duced model in order to estimate motion coefficients. The approach is first quantified on synthetic data: it demon-strates the interest of model reduction as a compromise be-tween results quality and computational cost. Results ob-tained on real data are then displayed so as to illustrate the method. 1