Abstract
In this paper, a general spatio-temporal framework for motion estimation is presented. It allows to estimate a fully parametric motion model over an image sequence. As parametric models describe one motion only, a robust estimator is introduced in order to cope with several moving objects. The motion segmentation algorithm combines luminance and the composition of all the motions detected over a set of successive frames for motion boundaries estimation.
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References
B. K. P. Horn and E. J. Weldon.Direct methods for recovering motion. International Journal of Computer Vision, 2 (1):51–76, 1988.
J. R. Bergen, P. Anandan, K. J. Hanna, and R. Hingorani. Hierarchical model-based motion estimation.In Second European Conference on Computer Vision, pages 237–252, Santa Margherita, Italy, May 1992.
B. Duc. Motion estimation using invariance under group transformations. In 12th International Conference on Pattern Recognition, pages 159–163, Jerusalem, Israel, October 9–13 1994.
G. W. Bluman and S. Kumei. Symmetries and Differential Equations, volume 81 of Applied Mathematical Sciences. Springer-Verlag, New York, 1989.
P.J. Rousseeuw and A.M. Leroy. Robust Regression and Outlier Detection. John Wiley and Sons, New York, 1987.
P. J. Rousseeuw and B. C. van Zomeren. Unmasking multivariate outliers and leverage points. Journal of the American Statistical Association, 85(411):633–639, 1990.
W.B Thompson. Combining motion and contrast for segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2:543–549, 1980.
M.J. Black. Combining intensity and motion for incremental segmentation and tracking over long image sequences. In ECCV-92 European Conference On Computer Vision, pages 485–493, Santa Margherita, Italy, May 1992.
S. Geman and D. Geman. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741, 1984.
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© 1995 Springer-Verlag Berlin Heidelberg
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Duc, B., Schroeter, P., Bigün, J. (1995). Spatio-temporal robust motion estimation and segmentation. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_302
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DOI: https://doi.org/10.1007/3-540-60268-2_302
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Online ISBN: 978-3-540-44781-8
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