A robust method for motion estimation in image sequences

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

Volumn 1899, Issue , 2000, Pages 108-119

  Mecke, R ^a   Michaelis, B ^a  

^a OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DEFORMATION; KALMAN FILTERS; KINEMATICS;

CONSTANT VELOCITIES; FALSE MEASUREMENTS; FILTERING ALGORITHM; MONOCULAR IMAGE SEQUENCE; MOTION TRAJECTORIES; NEURAL RECOGNITION; PARTIAL OCCLUSIONS; RECURSIVE ESTIMATION;

MOTION ESTIMATION;

EID: 35248872632     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/10722604_10     Document Type: Conference Paper

References (10)

1. Musmann, H. G.; Pirsch, P.; Grallert, H. J.: Advances in Picture Coding. Proc. of the IEEE, 1985, Volume 73, No. 4, 523-548.
2. Zhang, Z.: Token Tracking in a Cluttered Scene. Research Report N°2072, INRIA, ISSN 0249-6399, October 1993.
3. Blake, A. et al.: Affine-Invariant Contour Tracking With Automatic Control of Spatiotemporal Scale. In: Proc. Of 4. Int. Conf. on ComputerVision, Berlin, May 11th-14th, 1993, IEEE Computer Society Press, ISBN 0-8186-3870-2.
4. Maybeck, P. S.: Stochastic Models, Estimation and Control. Academic Press Inc., Vol. 1-3, ISBN 0-12-48070(1or2or3)-X, 1982.
5. Sorenson, H. W.: Kalman Filtering: Theory and Application. IEEE Press, ISBN 0- 87942-191-6, 1985.
6. Kalman, R. E.: A New Approach to Linear Filtering and Prediction Problems, 1960, In: [5] from this list, pp. 16-26.
7. Haykin, S.: Adaptive Filter Theorie. 3rd ed-, Prentice-Hall Inc., 1996.
8. Schnelting, O.; Michaelis, B.; Mecke, R.: Motion Estimation By Using Artificial Neural Networks, Proc. of IEEE-Workshop on Nonlinear Signal and Image Processing, Neos Marmaras, Greece, June 20th-22th, 1995, Vol. 2, pp. 714-717.
9. Mecke, R.: Grauwertbasierte Bewegungsschätzung in monokularen Bildsequenzen unter besonderer Berücksichtigung bildspezifischer Störungen, PhD Thesis, Ottovon- Guericke-University of Magdeburg, Department Electrical Engineering, 1999.
10. Rao, R. P. N.: Robust Kalman Filters for Prediction, Recognition, and Learning. Technical Report 645, University of Rochester, Computer Science Department, Rochester, New York, Dezember 1996.