Abstract
A model for computing image flow in image sequences containing a very wide range of instantaneous flows is proposed. This model integrates the spatio-temporal image derivatives from multiple temporal scales to provide both reliable and accurate instantaneous flow estimates. The integration employs robust regression and automatic scale weighting in a generalized brightness constancy framework. In addition to instantaneous flow estimation the model supports recovery of dense estimates of image acceleration and can be readily combined with parameterized flow and acceleration models. A demonstration of performance on image sequences of typical human actions taken with a high frame-rate camera is given.
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Adiv, G. 1985. Determining three-dimensional motion and structure from optical flow generated by several moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(4): 384–401.
Battiti, R., Amaldi, E., and Koch, C. 1991. Computing optical flow across multiple scales: an adaptive coarse-to-fine strategy. International Journal of Computer Vision, 2(6):133–145.
Beauchemin, S.S. and Barron, J.L. 1995. The computation of optical flow. ACM Computing Surveys, (27)3:433–467.
Bergen, J.R., Anandan, P., Hanna, K.J., and Hingorani, R. 1992. In Heirarchical Model-Based Motion Estimation, G. Sandini (Ed.), European Conference on Computer Vision, vol. 588 of LNCS-Series, Springer-Verlag, pp. 237–252.
Black, M.J. and Anandan, P. 1993. A frame-work for robust estimation of optical flow. International Conference on Computer Vision, pp. 231–236.
Black, M.J. and Anandan, P. 1996. The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding, 63(1):75–104.
Black, M.J. and Yacoob, Y. 1997. Recognizing facial expressions in image sequences using local parameterized models of image motion. International Journal of Computer Vision, 25(1):23–48.
Blake, A. and Zisserman, A. 1987. Visual Reconstruction, The MIT Press: Cambridge, Massachusetts.
Fleet, D.J. and Jepson, A.D. 1990. Computation of component image velocity from local phase information. International Journal of Computer Vision, 3(4):77–104.
Geman, S. and McClure, D.E. 1987. Statistical methods for tomographic image reconstruction. Bulletin of the International Statistical Institute, LII-4:5–21.
Heeger, D.J. 1988. Optical flow using spatio-temporal filters. International Journal of Computer Vision, 1:279–302.
Kearney, J., Thompson, W.B., and Boley, D.L. 1987. Optical flow estimation: an error analysis of gradient-based methods with local optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(2):229–244.
Lindeberg, T. 1994. Scale-Space Theory in Computer Vision. Kluwer Academic Publishers.
Lindeberg, T. 1996. A scale selection principle for estimating image deformations, Technical Report, Stockholm University, CVAP 196.
Singh, A. 1991. Incremental estimation of image flowusing a Kalman filter. IEEE Proceedings of theWorkshop onVisual Motion, pp. 36–43.
Weber, J. and Malik, J. 1995. Robust computation of optical flow in a multi-scale differenial framework. International Journal of Computer Vision, 14(1):67–81.
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Yacoob, Y., Davis, L.S. Temporal Multi-Scale Models for Flow and Acceleration. International Journal of Computer Vision 32, 147–163 (1999). https://doi.org/10.1023/A:1008109516258
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DOI: https://doi.org/10.1023/A:1008109516258