Learning parameterized models of image motion
1997
Conference Paper
ps
A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. Many complex image motions can be represented by a linear combination of a small number of these basis flows. The learned motion models may be used for optical flow estimation and for model-based recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, and articulated human motion.
Author(s): | Black, M. J. and Yacoob, Y. and Jepson, A. D. and Fleet, D. J. |
Book Title: | IEEE Conf. on Computer Vision and Pattern Recognition, CVPR-97 |
Pages: | 561-567 |
Year: | 1997 |
Month: | June |
Department(s): | Perceiving Systems |
Bibtex Type: | Conference Paper (inproceedings) |
Paper Type: | Conference |
Address: | Puerto Rico |
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BibTex @inproceedings{Black:IEEE:1997, title = {Learning parameterized models of image motion}, author = {Black, M. J. and Yacoob, Y. and Jepson, A. D. and Fleet, D. J.}, booktitle = {IEEE Conf. on Computer Vision and Pattern Recognition, CVPR-97}, pages = {561-567}, address = {Puerto Rico}, month = jun, year = {1997}, doi = {}, month_numeric = {6} } |