DocumentCode :
3428945
Title :
Optimal Orthogonal Basis and Image Assimilation: Motion Modeling
Author :
Huot, E. ; Papari, G. ; Herlin, I.
Author_Institution :
INRIA, Sophia-Antipolis, France
fYear :
2013
fDate :
1-8 Dec. 2013
Firstpage :
3352
Lastpage :
3359
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.
Keywords :
Galerkin method; data assimilation; image sequences; motion estimation; numerical analysis; Euler equations; Galerkin projection; computational cost; data assimilation method; image assimilation; image data; interest of model reduction; motion estimation; numerical computation; optical flow equations; optimal orthogonal basis; physical model; synthetic data; Boundary conditions; Data assimilation; Equations; Mathematical model; Motion estimation; Numerical models; Vectors; data assimilation; galerkin projection; motion estimation; reduced model; satellite image;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision (ICCV), 2013 IEEE International Conference on
Conference_Location :
Sydney, VIC
ISSN :
1550-5499
Type :
conf
DOI :
10.1109/ICCV.2013.416
Filename :
6751528
Link To Document :
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