Borst, A. & Euler, T. Seeing things in motion: models, circuits, and mechanisms.
Various motion models in Kalman filters have been studied for tracking.
This allows the use of realistic 3D motion models and easy incorporation of self-motion measurements.
Statistical methods employed for the analysis of the data and additive motion models are derived.
For applications involving dynamic analysis this hazard is frequently characterized through stochastic ground motion models.
A proper choice of motion models and a hierarchical organization of the iterations provide additional robustness.
Despite the importance of single particle motion in biological systems, systematic inference approaches to analyze particle trajectories and evaluate competing motion models are lacking.
Human motion models for reaching tasks facilitate the design of many systems such as computer and cellphone interfaces, cockpits, and assembly lines.
This process uses affine motion models to project regions from frame to frame and a color-based region growing to determine the final projected regions.
Finally, the motion equations are deduced from these motion models, and the closed-form solutions are figured out.
Description of the earthquake acceleration time history through stochastic ground motion models is considered in this context.
