Center for Human Modeling and Simulation

Document Type

Conference Paper

Date of this Version

June 1994


Copyright 1994 IEEE. Reprinted from Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 437-442.
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This paper develops a new class of physics-based deformable models which can deform both globally and locally. Their global parameters are functions allowing the definition of new parameterized primitives and parameterized global deformations. These new global parameter functions improve the accuracy of shape description through the use of a few intuitive parameters such as functional bending and twisting. Using a physics-based approach we convert these geometric models into deformable models that deform due to forces exerted from the datapoints so as to conform to the given dataset. We present an experiment involving the extraction of shape and motion of the Left Ventricle (LV) of a heart from MRI-SPAMM data based on a few global parameter functions.



Date Posted: 24 July 2007

This document has been peer reviewed.