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.

• Type: Conference
• Year: 1997
• Where: CVPR
• Authors: Michael J. Black, Yaser Yacoob, Allan D. Jepson, David J. Fleet
• Keywords: Complex Image Motions, Computer Vision, CVPR 1997, Flow Models, Learned Motion Models, Optical Flow Estimation, Orthogonal Basis Flow