Abstract
Global optical flow techniques minimize a mixture of two terms: a data term relating the observable signal with the optical flow, and a regularization term imposing prior knowledge/assumptions on the solution. A large number of different data terms have been developed since the first global optical flow estimator proposed by Horn and Schunk [1]. Recently [2], these data terms have been classified with respect to their properties. Thus, for image sequences where certain properties about image as well as motion characteristics are known in advance, the appropriate data term can be chosen from this classification. In this contribution, we deal with the situation where the optimal data term is not known in advance. We apply the Bayesian evidence framework for automatically choosing the optimal relative weight between two data terms as well as the regularization term based only on the given input signal.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)
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)
Haussecker, H., Fleet, D.: Computing optical flow with physical models of brightness variation. In: Proc. Computer Vision and Pattern Recognition (2000)
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)
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)
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)
Bigün, J., Granlund, G.H.: Optimal orientation detection of linear symmetry. In: Proc. ICCV, pp. 433–438. IEEE, Los Alamitos (1987)
Jähne, B.: Digital Image Processing, 4th edn. Springer, Heidelberg (1998)
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)
Weickert, J., Schnörr, C.: A theoretical framework for convex regularizers in pde-based computation of image motion. Int. J. Comput. Vision 45, 245–264 (2001)
Black, M.J., Anandan, P.: A framework for the robust estimation of optical flow. In: Proc. Fourth International Conf. on Computer Vision (ICCV 1993), Berlin, Germany, pp. 231–236 (1993)
Alvarez, L., Weickert, J., Sánchez, J.: Reliable estimation of dense optical flow fields with large displacements. Int. J. Comput. Vision 39 (2000)
Simoncelli, E.P.: Design of multi-dimensional derivative filters. In: Intern. Conf. on Image Processing, Austin TX (1994)
Lee, D.J., Ipsen, I.C.F.: Zone determinant expansions for nuclear lattice simulations. Phys. Rev. C 68, 64003 (2003)
Scharr, H.: Optimal Operators in Digital Image Processing. PhD thesis, Interdisciplinary Center for Scientific Computing, Univ. of Heidelberg (2000)
Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. Journal of Computer Vision 12, 43–77 (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Krajsek, K., Mester, R. (2007). Bayesian Model Selection for Optical Flow Estimation. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds) Pattern Recognition. DAGM 2007. Lecture Notes in Computer Science, vol 4713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74936-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-74936-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74933-2
Online ISBN: 978-3-540-74936-3
eBook Packages: Computer ScienceComputer Science (R0)