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Proceedings Paper

Physically based and probabilistic models for computer vision
Author(s): Richard Szeliski; Demetri Terzopoulos
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Paper Abstract

Models of 2-D and 3-D objects are an essential aspect of computer vision. Physically-based models represent object shape and motion through dynamic differential equations and provide mechanisms for fitting and tracking visual data using simulated forces. Probabilistic models allow the incorporation of prior knowledge about shape and the optimal extraction of information from noisy sensory measurements. In this paper we propose a framework for combining the essential elements of both the physically-based and probabilistic approaches. The combined model is a Kalman filter which incorporates physically-based models as part of the prior and system dynamics and is able to integrate noisy data over time. In particular, through a suitable choice of parameters models can be built which either return to a rest shape when external data are removed or remember shape cues seen previously. The proposed framework shows promise in a number of computer vision applications.

Paper Details

Date Published: 1 September 1991
PDF: 13 pages
Proc. SPIE 1570, Geometric Methods in Computer Vision, (1 September 1991); doi: 10.1117/12.48420
Show Author Affiliations
Richard Szeliski, Digital Equipment Corp. (United States)
Demetri Terzopoulos, Univ. of Toronto (Canada)

Published in SPIE Proceedings Vol. 1570:
Geometric Methods in Computer Vision
Baba C. Vemuri, Editor(s)

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