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Abstract: Gradient methods are widely used in the computation of optical flow. We discuss extensions of these methods which compute probability distributions of optical flow. The use of distributions allows representation of the uncertainties inherent in the optical flow computation, facilitating the combination with information from other sources. We compute distributed optical flow for a synthetic image sequence and demonstrate that the probabilistic model accounts for the errors in the flow estimates. ... (Update)
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BibTeX entry: (Update)
E. Simoncelli, E. Adelson, and D. Heeger, "Probability distributions of optical flow," in Proceedings of the IEEE Computer Vision and Pattern Recognition Conference, pp. 310--315, 1991. http://citeseer.ist.psu.edu/simoncelli91probability.html More
@inproceedings{ simoncelli91probability,
author = "E P Simoncelli and E H Adelson and D J Heeger",
title = "Probability Distributions of Optical Flow",
booktitle = "Proc Conf on Computer Vision and Pattern Recognition",
publisher = "IEEE Computer Society",
address = "Mauii, Hawaii",
pages = "310--315",
year = "1991",
url = "citeseer.ist.psu.edu/simoncelli91probability.html" }
Citations (may not include all citations):755 Determining optical flow (context) - Horn, Schunck - 1981 ACM DBLP
492 An iterative image registration technique with an applicatio.. - Lucas, Kanade - 1981
257 A computational framework and an algorithm for the measureme.. (context) - Anandan - 1989
83 Personal Communication (context) - Heeger - 1990
82 Optical flow using spatiotemporal filters (context) - Heeger - 1988
76 Fast filter transforms for image processing (context) - Burt - 1981
37 Hierarchical warp stereo (context) - Quam - 1984
4 Bayesian modeling of uncertainty in low-level vision (context) - Szeliski - 1990 ACM
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