Tracking with the EM contour algorithm

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

Volumn 2350, Issue , 2002, Pages 3-17

  Pece, Arthur E C ^a   Worrall, Anthony D ^b  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

^b UNIVERSITY OF READING   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

ACTIVE CONTOURS; CONTOUR ALGORITHMS; DYNAMICAL MODEL; GEOMETRIC MODELS; POSE PARAMETERS; SHAPE VARIABILITIES; SIMPLE MODELING; STATISTICAL DEPENDENCIES;

COMPUTER VISION;

EID: 84944390911     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47969-4_1     Document Type: Conference Paper

References (22)

1. Y. Bar-Shalom, T. E. Fortmann, Tracking And Data Association. Academic Press, 1988.
2. A. Baumberg, D.C. Hogg, Learning Flexible Models from Image Sequences, Proc. of ECCV’94, Lecture Notes in Computer Science, vol. 800, pp.299-308, 1994.
3. A Blake, M Isard, Active Contours, Springer-Verlag, Berlin 1998.
4. A.P. Dempster, N.M. Laird, D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm (with discussion), J. of the Royal Statistical Soc. B 39: 1-38, 1977.
5. JM Ferryman, AD Worrall, Proc. of the 1st IEEE International Workshop on Performance Evaluation in Tracking andSurv eillance: PETS 2000.
6. D Geman, B Jedynak, An active testing model for tracking roads in satellite images. IEEE Trans. PAMI 18(1): 1-14, 1996.
7. C Harris, Tracking with rigidmo dels. In Active Vision (A Blake, A Yuille, eds) pp.59-73. MIT Press, 1992.
8. J. Huang, D. Mumford, Statistics of natural images and models. In Proc. CVPR’99, vol.1, pp.541-547, 1999.
9. A. Lee, D. Mumford, An Occlusion Model generating Scale-Invariant Images. In Proc. Int. Workshop on Statistical and Computational Theories of Vision. http://www.cis.ohio-state.edu/ szhu/SCTV2001.html, 1999.
10. M Kass, A Witkin, D Terzopoulos, Snakes: active contour models. In Proc. ICCV’97, pp.259-268, 1987.
11. D Koller, K Daniilidis, H-H Nagel, Model-based object tracking in monocular image sequences of roadtraffic scenes. Int.J.Comp.Vis. 10(3): 257-281, 1993.
12. H Kollnig, H-H Nagel, 3D pose estimation by fitting image gradients directly to polyhedral models. In Proc. ICCV’95, pp.569-574, 1995.
13. G.J. McLachlan, T. Krishnan, The EM Algorithm and Extensions, Wiley, New York, 1997.
14. A.E.C. Pece, Generative-model-based tracking by cluster analysis of image differences. Acceptedfor publication in Robotics and Autonomous Systems.
15. AEC Pece, AD Worrall, A statistically-basedNewton methodfor pose refinement. Image and Vision Computing 16: 541-544, 1998.
16. AEC Pece, AD Worrall, A Newton methodfor pose refinement of 3D models. Proc 6th International Symposium on Intelligent Robotic Systems: SIRS’98, The University of Edinburgh.
17. A.E.C. Pece, A.D. Worrall, Tracking without feature detection. In [5], pp.29-37, 2000.
18. DL Ruderman, The statistics of natural images. Network 5: 517-548, 1994.
19. H. Sidenbladh, M.J. Black, Learning image statistics for Bayesian tracking, In Proc. ICCV’01, Vol. 2, pp.709-716, 2001.
20. P. Tissainayagam, D. Suter, Tracking multiple object contour with automatic motion model switching, In Proc. ICPR’00, pp.1146-1149, 2000.
21. AD Worrall, JM Ferryman, GD Sullivan, KD Baker, Pose andstructure recovery using active models. Proc. BMVC’95, pp.137-146, 1995.
22. AL Yuille, JM Coughlan, Fundamental limits of Bayesian inference: order parameters andphase transitions for roadtrac king. IEEE Trans. PAMI 22(2): 160-173, 2000.