Name
Abstract | ||
|---|---|---|
In this work, statistical based motion estimation is applied to the problem of motion estimation for video coding. We show that the motion equations of a rigid body can be formulated as a nonlinear dynamic system whose state is represented by the motion parameters and by the scaled depths of the object feature points. An extended Kalman filter is used to estimate the global motion, from which successive frames can be predicted in a motion compensated video coding system. The structure imposed by the model implies that the reconstructed motion is very natural in comparison to more common block-based schemes. Moreover, the parametrization of the model allows for a very efficient coding of motion information |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/ICIP.1996.585570 | Image Processing, 1996. Proceedings., International Conference |
Keywords | Field | DocType |
Kalman filters,filtering theory,image sequences,motion compensation,motion estimation,nonlinear dynamical systems,prediction theory,statistical analysis,video coding,extended Kalman filter,global motion,motion compensated system,motion parameters,nonlinear dynamic system,object feature points,rigid body motion equations,scaled depths,statistical based motion estimation,successive frames,video coding | Structure from motion,Computer vision,Extended Kalman filter,Block-matching algorithm,Motion field,Quarter-pixel motion,Computer science,Motion compensation,Kalman filter,Artificial intelligence,Motion estimation | Conference |
Volume | ISBN | Citations |
1 | 0-7803-3259-8 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Calvagno | 1 | 130 | 26.32 |
| Luca Celeghin | 2 | 0 | 0.34 |
| R Rinaldo | 3 | 78 | 18.32 |
| Luciano Sbaiz | 4 | 84 | 11.42 |