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.

[1]  Daphna Weinshall,et al.  Motion of disturbances: detection and tracking of multi-body non-rigid motion , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  M. Carter.  Computer graphics: Principles and practice , 1997 .

[3]  Edward H. Adelson,et al.  A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Jezekiel Ben-Arie,et al.  Pictorial recognition using affine-invariant spectral signatures , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  James W. Davis,et al.  The representation and recognition of human movement using temporal templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Hans-Hellmut Nagel,et al.  Algorithmic characterization of vehicle trajectories from image sequences by motion verbs , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Nuno Vasconcelos,et al.  Empirical Bayesian EM-based motion segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  David J. Fleet,et al.  Learning parameterized models of image motion , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Mubarak Shah,et al.  Visual gesture recognition , 1994 .

[10]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  V. Leitáo,et al.  Computer Graphics: Principles and Practice , 1995 .

[12]  K. Raghunath Rao,et al.  Multiple template matching using the expansion filter , 1994, IEEE Trans. Circuits Syst. Video Technol..

[13]  Hung-Tat Tsui,et al.  Feature tracking from an image sequence using geometric invariants , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.