This paper considers the problem of motion tracking of deformable contours based on synthesis and match. We propose framework which encodes specific information on shape, deformation and motion of the target object. Using this information, the trackers synthesize contours that are most likely to describe the object in the current frame, and perform localization operations to select the best match templates. We present three trackers: the first imposes affine motion smoothness constraints, the second employs principal component analysis to synthesize a codebook of contour templates, while the third combines these ideas to synthesize templates along several major modes of motion. The resulting trackers require only a few parameters to characterize the motion. They are thus suitable for very low bit rate visual communication tasks. Preliminary applications in model-based coding have been attempted.

This paper considers the problem of modeling and extracting arbitrary deformable contours from noisy images. We propose a global contour model based on a stable and regenerative shape matrix, which is invariant and unique under rigid motions. Combined with Markov random field to model local deformations, this yields prior distribution that exerts influence over a global model while allowing for deformations. We then cast the problem of extraction into posterior estimation and show its equivalence to energy minimization of a generalized active contour model. We discuss pertinent issues in shape training, energy minimization, line search strategies, minimax regularization and initialization by generalized Hough transform. Finally, we present experimental results and compare its performance to rigid template matching.>

This paper considers the problem of modeling and extracting arbitrary deformable contours from noisy images. We propose a global contour model based on a stable and regenerative shape matrix, which is invariant and unique under rigid motions. Combined with Markov random field to model local deformations, this yields prior distribution that exerts influence over a global model while allowing for deformations. We then cast the problem of extraction into posterior estimation and show its equivalence to energy minimization of a generalized active contour model. We discuss pertinent issues in shape training, minimax regularization and initialization by generalized Hough transform. Finally, we present experimental results and compare its performance to rigid template matching.>