Particle filtering with evidential reasoning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2238, Issue , 2002, Pages 305-316

  Eveland, Christopher K ^a  

^a UNIVERSITY OF ROCHESTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INTELLIGENT ROBOTS; MONTE CARLO METHODS;

CONDENSATION ALGORITHM; CONDITIONAL PROBABILITIES; EVIDENTIAL REASONING; EXPLICIT MODELING; MULTIPLE SENSORS; PARTICLE FILTERING; TRACKING ALGORITHM; VISION COMMUNITIES;

KALMAN FILTERS;

EID: 84949205018     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45993-6_17     Document Type: Conference Paper

References (11)

1. Mathias Bauer. Approximations for decision making in the dempster-shafer theory of evidence. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, pages 73–80, 1996
2. Michael J. Black and David J. Fleet. Probabilistic detection and tracking of motion discontinuities. In IEEE 7th International Conference on Computer Vision, volume 1, pages 551–558, 1999
3. M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density. In Proceedings ECCV, 1996
4. M. Isard and A Blake. Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision, 29(1):5–28, 1998
5. R. E. Kalman. A new approach to linear filtering and prediction problems. Transactions of the ASME Journal of Basic Engineering, pages 35–45, March 1960
6. Kurt Konolige. Small vision systems: Hardware and implementation. In The Eights International Symposium of Robotics Research, October 1997
7. John MacCormick. Probabilistic Modeling and Stochastic Algorithms for Visual Localisation and Tracking. PhD thesis, University of Oxford, January 2000
8. Robin R. Murphy. Adaptive rule of combination for observations over time. In Multisensor Fusion and Integration for Intelligent Systems, 1996
9. Glenn Shafer. A Mathematical Theory of Evidence. Princeton University Press, 1976
10. Thomas M. Strat. Continuous belief functions for evidential reasoning. In AAAI, pages 308–313, August 1984
11. Doug Y. Suh. Transformation of mass function and joint mass function for evidence theory in the continuous domain. Journal of Mathematical Analysis and Applications, 176:521–544, 1993