Abstract
This paper introduces a new algorithm for computing multi-resolution optical flow, and compares this new hierarchical method with the traditional combination of the Lucas-Kanade method with a pyramid transform. The paper shows that the new method promises convergent optical flow computation. Aiming at accurate and stable computation of optical flow, the new method propagates results of computations from low resolution images to those of higher resolution. The resolution of images increases this way for the sequence of images used in those calculations. The given input sequence of images defines the maximum of possible resolution.
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References
Anandan, P.: A computational framework and an algorithm for the measurement of visual motion. Int. J. Comput. Vision 2, 283–310 (1989)
Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. J. Comput. Vision 12, 43–77 (1995)
Bouguet, J.: Pyramidal implementation of the Lucas-Kanade feature tracker. Intel Corporation, Microprocessor Research Labs, OpenCV Documents (1999)
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)
Handschack, P., Klette, R.: Quantitative comparisons of differential methods for measuring image velocity. In: Proc. Aspects of Visual Form Processing, Capri, pp. 241–250 (1994)
Hwang, S.-H., Lee, U.K.: A hierarchical optical flow estimation algorithm based on the interlevel motion smoothness constraint. Pattern Recognition 26, 939–952 (1993)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. Imaging Understanding Workshop, pp. 121–130 (1981)
Mahzoun, M.R., et al.: A scaled multigrid optical flow algorithm based on the least RMS error between real and estimated second images. Pattern Recognition 32, 657–670 (1999)
Nagel, H.-H.: On the estimation of optical flow: Relations between different approaches and some new results. Artificial Intelligence 33, 299–324 (1987)
Ruhnau, P., et al.: Variational optical flow estimation for particle image velocimetry. Experiments in Fluids 38, 21–32 (2005)
Weber, J., Malik, J.: Robust computation of optical flow in a multi-scale differential framework. Int. J. Comput. Vision 14, 67–81 (1995)
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© 2008 Springer-Verlag Berlin Heidelberg
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Ohnishi, N., Kameda, Y., Imiya, A., Dorst, L., Klette, R. (2008). Dynamic Multiresolution Optical Flow Computation. In: Sommer, G., Klette, R. (eds) Robot Vision. RobVis 2008. Lecture Notes in Computer Science, vol 4931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78157-8_1
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DOI: https://doi.org/10.1007/978-3-540-78157-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78156-1
Online ISBN: 978-3-540-78157-8
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