• Corpus ID: 17388300

Kalman filter in computer vision

@inproceedings{Klmn2011KalmanFI,
  title={Kalman filter in computer vision},
  author={Rudolf E. K{\'a}lm{\'a}n and Thorvald Nicolai Thiele and Richard S. Bucy},
  year={2011}
}
In statistics, the Kalman filter is a mathematical method named after Rudolf E. Kalman. Its purpose is to use measurements that are observed over time that contain noise (random variations) and other inaccuracies, and produce values that tend to be closer to the true values of the measurements and their associated calculated values. The Kalman filter has many applications in technology, and is an essential part of the development of space and military technology. Perhaps the most commonly used… 
1 Citations
Kalman Filtering and Its Real‐Time Applications
TLDR
This chapter outlined and explained the fundamental Kalman filtering model in real‐time discrete form and devised two real-time applications that implement‐ ed Kalman filter.

References

SHOWING 1-10 OF 55 REFERENCES
Kalman Filtering with Real-time Applications
TLDR
Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection.
New extension of the Kalman filter to nonlinear systems
TLDR
It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
SCAAT: incremental tracking with incomplete information
TLDR
The introduction and exploration of the SCAAT approach to 3D tracking for virtual environments is introduced, which facilitates user motion prediction, multisensor data fusion, and in systems where the observations are only available sequentially it provides estimates at a higher rate and with lower latency than a multiple-constraint approach.
Kalman Filtering: Theory and Practice Using MATLAB
TLDR
Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering and appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.
The unscented Kalman filter for nonlinear estimation
  • E. Wan, R. Van Der Merwe
  • Mathematics
    Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373)
  • 2000
This paper points out the flaws in using the extended Kalman filter (EKE) and introduces an improvement, the unscented Kalman filter (UKF), proposed by Julier and Uhlman (1997). A central and vital
Block Kalman Filtering for Large-Scale DSGE Models
TLDR
Block filtering compares favourably with the more general method for faster Kalman filtering outlined by Koopman and Durbin and, furthermore, the two approaches are largely complementary.
Estimation with Applications to Tracking and Navigation
TLDR
Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations using a balanced combination of linear systems, probability, and statistics.
Non-Linear DSGE Models, the Central Difference Kalman Filter, and the Mean Shifted Particle Filter
This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Difference Kalman Filter (CDKF). The advantage of this
An innovations approach to least-squares estimation--Part II: Linear smoothing in additive white noise
The innovations approach to linear least-squares approximation problems is first to "whiten" the observed data by a causal and invertible operation, and then to treat the resulting simpler
Robust bayesian estimation for the linear model and robustifying the Kalman filter
Starting with the vector observation model y = Hx + v , robust Bayesian estimates \hat{x} of the vector x are constructed for the following two distinct situations: 1) the state x is Gaussian and the
...
...