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Proceedings Paper

Invariant reconstruction of 3-D curves and surfaces
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Paper Abstract

The reconstruction of curves and surfaces from sparse data is an important task in many applications. In computer vision problems the reconstructed curves and surfaces generally represent some physical property of a real object in a scene. Thus the characteristics of the reconstruction process differs from straight forward fitting of smooth curves and surfaces to a set of data. Since the collected data is represented in an arbitrarily chosen coordinate system the reconstruction process should be invariant to the choice of the coordinate system (except for the transformation between the two coordinate systems). In this paper reconstruction algorithms are presented for reconstructing invariant estimates of both curves and surfaces. The reconstruction problem will be cast as an illposed inverse problem which must be stablized using a priori information about the constraint formation. Tikhonov regularization is used to form a wellposed mathematical problem statement. Examples of typical reconstructed objects are also given. 1.

Paper Details

Date Published: 1 February 1991
PDF: 12 pages
Proc. SPIE 1382, Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods, (1 February 1991); doi: 10.1117/12.25229
Show Author Affiliations
Robert L. Stevenson, Univ. of Notre Dame (United States)
Edward J. Delp, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 1382:
Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods
David P. Casasent, Editor(s)

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