Accurate Dense Optical Flow Estimation Using Adaptive Structure Tensors and a Parametric Model

IEEE Transactions on Image Processing

Volumn 12, Issue 10, 2003, Pages 1170-1180

  Liu, Haiying ^a   Chellappa, Rama ^a   Rosenfeld, Azriel ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

Generalized eigenvalue; Optical flow; Parametric model; Structure tensor

Indexed keywords

ADAPTIVE SYSTEMS; ALGORITHMS; EIGENVALUES AND EIGENFUNCTIONS; PARAMETER ESTIMATION; TENSORS;

STRUCTURE TENSORS;

OPTICAL FLOWS;

EID: 0141953953     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2003.815296     Document Type: Article

References (15)

1. J. L. Barron, D. J. Fleet, and S. S. Beauchemin, "Performance of optical flow techniques," Int. J. Comput. Vis., vol. 12, pp. 43-77, 1994.
2. G. Farnebäck, "Fast and accurate motion estimation using orientation tensors and parametric motion models," in Proc. Int. Conf. Pattern Recognition, vol. 1, 2000, pp. 135-139.
3. _, "Very high accuracy velocity estimation using orientation tensors, parametric motion, and simultaneous segmentation of the motion field," in Proc. Int. Conf. Computer Vision, vol. 1, 2001, pp. 77-80.
4. B. Jähne, H. Haussecker, H. Spies, D. Schmundt, and U. Schurr, "Study of dynamical processes with tensor-based spatiotemporal image processing techniques," in Proc. ECCV, vol. 2, 1998, pp. 322-336.
5. H. Liu, T. H. Hong, M. Herman, and R. Chellappa, "A general motion model and spatio-temporal filters for computing optical flow," Int. J. Computer Vis., vol. 22, pp. 141-172, 1997.
6. H. Spies and H. Scharr, "Accurate optical flow in noisy image sequences," Proc. Int. Conf. Computer Vision, vol. 1, pp. 587-592, 2001.
7. G. H. Golub and C. F. V. Loan, Matrix Computations, 3rd ed. Baltimore, MD: Johns Hopkins Univ. Press, 1996.
8. H. W. Haussecker and D. J. Fleet, "Computing optical flow with physical models of brightness variation," IEEE Trans. Pattern Anal. Machine Intell., vol. 23, pp. 661-673, 2001.
9. B. Jähne, "Image sequence analysis of complex physical objects: Non-linear small scale water surface waves," in Proc. Int. Conf. Computer Vision, vol. 2, 1987, pp. 191-200.
10. B. Matei and P. Meer, "A general method for errors-in-variables problems in computer vision," Proc. Computer Vision Pattern Recognition, vol. 2, pp. 18-25, 2000.
11. O. Nestares, D. J. Fleet, and D. J. Heeger, "Likelihood functions and confidence bounds for total-least-squares problems," Proc. Computer Vision Pattern Recognition, vol. 1, pp. 523-530, 2000.
12. J. Weber and J. Malik, "Robust computation of optical flow in a multi-scale differential framework," Int. J. Comput. Vis., vol. 14. pp. 5-19. 1995.
13. G. Farneback, ''Spatial Domain Methods for Orientation and Velocity Estimation," Ph.D., Dept. Electron. Eng., Linköping University, Sweden, 1999.
14. H. Haussecker and H. Spies, Handbook of Computer Vision and Applications Volume 2: Signal Processing and Pattern Recognition. New York: Academic, 1999, ch. 13.
15. B. Jähne, Spatio-Temporal Image Processing, Theory and Scientific Applications. Berlin, Germany: Springer-Verlag, 1993.