Empirical choice of smoothing parameters in robust optical flow estimation

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 3, Issue , 2004, Pages

  Shi, Mingren ^a   Solo, Victor ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FUNCTIONS; IMAGE PROCESSING; ITERATIVE METHODS; OPTIMIZATION; PARAMETER ESTIMATION; PROBLEM SOLVING; REGRESSION ANALYSIS; ROBUSTNESS (CONTROL SYSTEMS);

BRIGHTNESS CONSTRAINT EQUATIONS (BCE); GIBBS RINGING; OPTICAL FLOW ESTIMATION; VELOCITY ESTIMATION;

MOTION ESTIMATION;

EID: 4544384745     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. B.K.P. Horn, Robot Vision, Massachusetts, MIT Press, Cambridge, 1986.
2. M J Black and P Anandan, "The robust estimation of multiple motions: parametric and piecewise-smooth flow fields," Comp Vis Image Understanding, vol. 63, pp. 75-104, 1996.
3. R.M. Haralick and L.G. Shapiro, Computer and Robot Vision, Vol II, Addison-Wesley, New York, 1993.
4. J. Weber and J. Malik, "Robust computation of optical flow in a multi-scale differential framework," Int Jl Comp Vision, vol. 14, pp. 67-81, 1995.
5. B.K.P. Horn and B.G. Schunck, "Determining optical flow," Artificial Intelligence, vol. 17, pp. 185-203, 1981.
6. B. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," in DARPA Image Understanding Workshop. DARPA, 1981, pp. 121-130.
7. A. Tikhonov and V. Arsenin, Solutions of Ill-Posed Problems, Wiley, New York, 1977.
8. V.A. Morozov, Regularization Methods for Ill-posed Problems, CRC Press, Boca Raton, 1993.
9. M.P. Wand and M.C. Jones, Kernel Smoothing, Chapman and Hall, London, 1995.
10. Z Zhou, R Leahy, and J Qi, "Approximate maximum likelihood hyperparameter estimation for gibbs priors," vol. 6, pp. 844-861, 1997.
11. AM Thompson and J Kay, "On some Bayesian choices of regularisation parameter in image restoration," Inverse problems, vol. 9, pp. 749-761, 1993.
12. V. Solo, "A SURE-fired way to choose smoothing parameters in ill-conditioned inverse problems," in Proc. IEEE ICIP96. IEEE, 1996, IEEE Press.
13. R Szeliski, Bayesian Modeling of Uncertainty in Low-Level Vision, Kluwer Academic Publishers, Amsterdam, 1989.
14. M.G. Kang and A.K. Katsaggelos, "General choice of the regularization functional in regularised image restoration," IEEE Trans. Im.Proc., vol. 4, pp. 594-602, 1995.
15. V V Estrela and N P Galatsanos, "Spatially-adaptive regularized pel-recursive motion estimation based on cross-validation," in Proc IEEE ICIP98. IEEE, 1998, pp.-.
16. L. Ng and V. Solo, "A data-driven method for choosing smoothing parameters in optical flow problems," in Proc. IEEE ICIP97. IEEE Press, 1997, pp. III:360-363.
17. L Ng and V Solo, "A data driven smoothing parameter selection technique for optical flow estimation," in Proc IEEE ICIP2000, Seattle. IEEE, 2000.
18. A.B. Hadiashar and D. Suter, "Robust optic flow computation," Int Jl Comp Vision, vol. 29, pp. 59-77, 1998.
19. R G Staudte and S J Sheather, Robust Estimation and Testing, J Wiley, New York, 1990.
20. J.L. Barron, D.J. Fleet, and S.S. Beauchemin, "Systems and experiment: Performance of optical flow techniques," Int Jl Comp Vision, vol. 12, pp. 43-74, 1994.