Bayesian model selection for optical flow estimation

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

Volumn 4713 LNCS, Issue , 2007, Pages 142-151

  Krajsek, Kai ^a   Mester, Rudolf ^a  

^a GOETHE UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; OPTICAL FLOWS; PARAMETER ESTIMATION;

BAYESIAN EVIDENCE FRAMEWORK; IMAGE SEQUENCES; REGULARIZATION TERM IMPOSING;

BAYESIAN NETWORKS;

EID: 38149057569     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74936-3_15     Document Type: Conference Paper

References (16)

1. Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185-204 (1981)
2. Weickert, J., Bruhn, A., Brox, T., Papenberg, N.: A survey on variational optic flow methods for small displacements. In: Scherzer, O. (ed.) Mathematical models for registration and applications to medical imaging, pp. 103-136 (2006)
3. Haussecker, H., Fleet, D.: Computing optical flow with physical models of brightness variation. In: Proc. Computer Vision and Pattern Recognition (2000)
4. Krajsek, K., Mester, R.: Marginalized maximum a posteriori hyper-parameter estimation for global optical flow techniques. In: Bayesian Inference and Maximum Entropy Methods In Science and Engineering, Paris, France (2006)
5. Krajsek, K., Mester, R.: A maximum likelihood estimator for choosing the regularization parameters in global optical flow methods. In: IEEE International Conference on Image Processing, Atlanta, USA (2006)
6. Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. Seventh International Joint Conference on Artificial Intelligence, Vancouver, Canada, August 1981, pp. 674-679 (1981)
7. Bigün, J., Granlund, G.H.: Optimal orientation detection of linear symmetry. In: Proc. ICCV, pp. 433-438. IEEE, Los Alamitos (1987)
8. Jahne, B.: Digital Image Processing, 4th edn. Springer, Heidelberg (1998)
9. Simoncelli, E., Adelson, E.H., Heeger, D.J.: Probability distribution of optical flow. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, pp. 310-315. IEEE Computer Society Press, Los Alamitos (1991)
10. Weickert, J., Schnörr, C.: A theoretical framework for convex regularizers in pdebased computation of image motion. Int. J. Comput. Vision 45, 245-264 (2001)
11. Black, M.J., Anandan, P.: A framework for the robust estimation of optical flow. In: Proc. Fourth International Conf. on Computer Vision (ICCV93), Berlin, Germany, pp. 231-236 (1993)
12. Alvarez, L., Weickert, J., Sánchez, J.: Reliable estimation of dense optical flow fields with large displacements. Int. J. Comput. Vision 39 (2000)
13. Simoncelli, E.P.: Design of multi-dimensional derivative filters. In: Intern. Conf. on Image Processing, Austin TX (1994)
14. Lee, D.J., Ipsen, I.CF.: Zone determinant expansions for nuclear lattice simulations. Phys. Rev. C 68, 64003 (2003)
15. Scharr, H.: Optimal Operators in Digital Image Processing. PhD thesis, Interdisciplinary Center for Scientific Computing, Univ. of Heidelberg (2000)
16. Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. Journal of Computer Vision 12, 43-77 (1994)