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Proceedings Paper

Shape, Depth, And Nonrigid Motion From Profiles
Author(s): Demetri Terzopoulos; Andrew Witkin; Michael Kass
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Paper Abstract

Inferring the 3D structures of nonrigidly moving objects from natural images is a difficult yet basic problem in computational vision. Our approach makes use of dynamic, elastically deformable models. These physically-based 3D models offer the geometric flexibility to satisfy a diversity of visual constraints. Constraints are encoded as forces which act on the models to mold their shapes, place them in proper depth, and carry them through motions so as to best account for the available image data. We demonstrate the recovery of shape, depth, and nonrigid motion from object profiles (occluding contours) in natural images. This article reviews our approach; mathematical details are found in the primary sources.

Paper Details

Date Published: 29 March 1988
PDF: 11 pages
Proc. SPIE 0937, Applications of Artificial Intelligence VI, (29 March 1988); doi: 10.1117/12.946981
Show Author Affiliations
Demetri Terzopoulos, Schlumberger Palo Alto Research (United States)
Andrew Witkin, Schlumberger Palo Alto Research (United States)
Michael Kass, Schlumberger Palo Alto Research (United States)


Published in SPIE Proceedings Vol. 0937:
Applications of Artificial Intelligence VI
Mohan M. Trivedi, Editor(s)

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