Towards ultimate motion estimation: Combining highest accuracy with real-time performance

Proceedings of the IEEE International Conference on Computer Vision

Volumn I, Issue , 2005, Pages 749-755

  Bruhn, Andrés ^a   Weickert, Joachim ^a  

^a SAARLAND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; NUMERICAL METHODS; OPTICAL FLOWS; OPTIMIZATION; PROBLEM SOLVING; REAL TIME SYSTEMS; VARIATIONAL TECHNIQUES;

ANGULAR ERRORS; REAL-TIME PERFORMANCES; REAL-TIME VISION; VARIATIONAL METHODS;

MOTION ESTIMATION;

EID: 33745826128     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2005.240     Document Type: Conference Paper

References (30)

1. L. Alvarez, J. Weickert, and J. Sánchez. Reliable estimation of dense optical flow fields with large displacements. International Journal of Computer Vision, 39(1):41-56, Aug. 2000.
2. P. Anandan. A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision, 2:283-310, 1989.
3. J. L. Barron, D. J. Fleet, and S.S. Beauchemin. Performance of optical flow techniques. International Journal of Computer Vision, 12(1):43-77, Feb. 1994.
4. M. J. Black and P. Anandan. Robust dynamic motion estimation over time. In Proc. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 292-302, Maui, HI, June 1991. IEEE Computer Society Press.
5. M. J. Black and P. Anandan. The robust estimation of multiple motions: parametric and piecewise smooth flow fields. Computer Vision and Image Understanding, 63(1):75-104, Jan. 1996.
6. A. Borzi, K. Ito, and K. Kunisch. Optimal control formulation for determining optical flow. SIAM Journal on Scientific Computing, 24(3):818-847, 2002.
7. A. Brandt. Multi-level adaptive solutions to boundary-value problems. Mathematics of Computation, 31(138):333-390, Apr. 1977.
8. W. L. Briggs, V. E. Henson, and S. F. McCormick. A Multigrid Tutorial. SIAM, Philadelphia, second edition, 2000.
9. T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optic flow estimation based on a theory for warping. In T. Pajdla and J. Matas, editors, Computer Vision - ECCV 2004, volume 3024 of Lecture Notes In Computer Science, pages 25-36. Springer, Berlin, 2004.
10. A. Bruhn, J. Weickert, C. Feddern, T. Kohlberger, and C. Schnorr. Variational optical flow computation in real-time. IEEE Transactions on Image Processing, 14(5):608-615, May 2005.
11. A. Bruhn, J. Weickert, T. Kohlberger, and C. Schnorr. Discontinuity-preserving computation of variational optic flow in real-time. In R. Kimmel, N. Sochen, and J. Weickert, editors, Scale-Space and PDE Methods In Computer Vision, volume 3459 of Lecture Notes In Computer Science, pages 279-290. Springer, Berlin, 2005.
12. A. Bruhn, J. Weickert, and C. Schnörr. Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. International Journal of Computer Vision, 61(3):211-231, 2005.
13. R. H. Chan, T. F. Chan, and W. L. Wan. Multigrid for differential- convolution problems arising from image processing. In Proc. Workshop on Scientific Computing, pages 58-72, Hong Kong, Sept. 1997.
14. T. F. Chan and P. Mulet. On the convergence of the lagged diffusivity fixed point method in total variation image restoration. SIAM Journal on Numerical Analysis, 36(2):354-367, 1999.
15. I. Cohen. Nonlinear variational method for optical flow computation. In Proc. Eighth Scandinavian Conference on Image Analysis, volume 1, pages 523-530, Tromsø, Norway, May 1993.
16. W. Enkelmann. Investigation of multigrid algorithms for the estimation of optical flow fields in image sequences. Computer Vision, Graphics and Image Processing, 43:150-177, 1987.
17. C. Frohn-Schnauf, S. Henn, and K. Witsch. Nonlinear multigrid methods for total variation denoising. Computating and Visualization in Sciene, 7(3-4):199-206, 2004.
18. S. Ghosal and P. Č. Vaněk. Scalable algorithm for discontinuous optical flow estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(2):181-194, Feb. 1996.
19. F. Glazer. Multilevel relaxation in low-level computer vision. In A. Rosenfeld, editor, Multiresolution Image Processing and Analysis, pages 312-330. Springer, Berlin, 1984.
20. W. Hackbusch. Multigrid Methods and Applications. Springer, New York, 1985.
21. R. Kimmel and I. Yavneh. An algebraic multigrid approach for image analysis. SIAM Journal on Applied Mathematics, 24(4):1218-1231, 2003.
22. E. Mémin and P. Pèrez. A multigrid approach for hierarchical motion estimation. In Proc. 6th International Conference on Computer Vision, pages 933-938, Bombay, India, Jan. 1998.
23. H.-H. Nagel and W. Enkelmann. An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:565-593, 1986.
24. L. I. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268, 1992.
25. C. Schnörr. Segmentation of visual motion by minimizing convex non-quadratic functional. In Proc. Twelfth International Conference on Pattern Recognition, volume A, pages 661-663, Jerusalem, Israel, Oct. 1994. IEEE Computer Society Press.
26. D. Terzopoulos. Image analysis using multigrid relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(2):129-139, Mar. 1986.
27. U. Trottenberg, C. Oosterlee, and A. Schüller. Multigrid. Academic Press, San Diego, 2001.
28. C. R. Vogel. Computational Methods for Inverse Problems. SIAM, Philadelphia, 2002.
29. J. Weickert and C. Schnörr. A theoretical framework for convex regularizers in PDE-based computation of image motion. International Journal of Computer Vision, 45(3):245-264, Dec. 2001.
30. P. Wesseling. An Introduction to Multigrid Methods. Wiley, Chichester, 1992.