Filtering and smoothing for motion trajectory of feature point using non-Gaussian state space model

IEICE Transactions on Information and Systems

Volumn E84-D, Issue 6, 2001, Pages 755-759

  Ichimura, Naoyuki ^a   Ikoma, Norikazu ^b  

^a NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^b KYUSHU INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Feature point tracking; Image sequence; Non Gaussian state space model; Sequential Monte Carlo method

Indexed keywords

FEATURE EXTRACTION; KALMAN FILTERING; MATHEMATICAL MODELS; MATRIX ALGEBRA; MONTE CARLO METHODS; SPURIOUS SIGNAL NOISE; STATE SPACE METHODS; TRAJECTORIES;

FEATURE POINT TRACKING;

IMAGE SEGMENTATION;

EID: 0035362496     PISSN: 09168532     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (8)

1. Robust Bayesian estimation for the liner model and robustifying the Kalman filter
2. Novel approach to nonlinear/non-Gaussian Bayesian state estimation
3. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models
4. Condensation - Conditional density propagation for visual tracking
5. Sequential Monte Carlo methods for dynamic systems
6. Likelihood and the Bayes procedures
7. A corner orientation detector