Shape and nonrigid motion estimation through physics-basedsynthesis
Metaxas, D.
Terzopoulos, D.
Dept. of Comput. & Inf. Sci., Pennsylvania Univ., Philadelphia, PA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 1993
Volume: 15,
Issue: 6
On page(s): 580-591
ISSN: 0162-8828
References Cited: 29
CODEN: ITPIDJ
INSPEC Accession Number: 4465223
Digital Object Identifier: 10.1109/34.216727
Current Version Published: 2002-08-06
Abstract
A physics-based framework for 3-D shape and nonrigid motion
estimation for real-time computer vision systems is presented. The
framework features dynamic models that incorporate the mechanical
principles of rigid and nonrigid bodies into conventional geometric
primitives. Through the efficient numerical simulation of Lagrange
equations of motion, the models can synthesize physically correct
behaviors in response to applied forces and imposed constraints.
Applying continuous Kalman filtering theory, a recursive shape and
motion estimator that employs the Lagrange equations as a system model
is developed. The system model continually synthesizes nonrigid motion
in response to generalized forces that arise from the inconsistency
between the incoming observations and the estimated model state. The
observation forces also account formally for instantaneous uncertainties
and incomplete information. A Riccati procedure updates a covariance
matrix that transforms the forces in accordance with the system dynamics
and prior observation history. Experiments involving model fitting and
tracking of articulated and flexible objects from noisy 3-D data are
described
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