3D motion estimation using expansion matching and KL based canonical images

This paper describes a novel approach to 3D motion estimation of planar objects based on eigen-normalization, expansion matching (EXM) and a scaled orthographic projection model. Our approach leads to a comprehensive temporal description of all degrees of freedom in 3D (3 rotations and 3 translations). The 3D motion parameters of the objects are approximated by the corresponding affine parameters. The objects in each frame of a video sequence are normalized to a set of canonical images using principal component normalization procedure. The normalization approach here is based on principal components of the intensity weighted spatial values and not on the intensity values as in works such as eigenfaces. The canonical images generated differ only in orientation. Expansion matching (EXM) is then used to find the differences in orientation. Affine transformations between the shapes also are derived. The pose of the shape in 3D space can therefore be estimated. Experiments on video sequences of planar and quasi-planar objects show robust estimation of the real 3D rotations and translations of the objects in motion