Sciweavers

Share
CVPR
1997
IEEE

Learning Parameterized Models of Image Motion

10 years 7 months ago
Learning Parameterized Models of Image Motion
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, nonrigid motion of human mouths, and articulated human motion.
Michael J. Black, Yaser Yacoob, Allan D. Jepson, D
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 1997
Where CVPR
Authors Michael J. Black, Yaser Yacoob, Allan D. Jepson, David J. Fleet
Comments (0)
books