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Proceedings Paper

Issues for data reduction of dense three-dimensional data
Author(s): Joseph H. Nurre; Jennifer J. Whitestone; Dennis B. Burnsides; David M. Hoeferlin
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Paper Abstract

Acquiring a large quantity of 3D data has become common plane with the advent of new technologies. Reducing the number of data points improves processing speed and storage requirements. Astute data reduction requires an understanding of the correlation between data measures and geometric measures. These relationships are dependent upon the data reduction algorithm used. This paper investigates these relationships for a small number of data reduction algorithms. A framework is presented for tracking these changes and for assisting a user in identifying the most appropriate data reduction method for their application.

Paper Details

Date Published: 3 October 1995
PDF: 8 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222715
Show Author Affiliations
Joseph H. Nurre, Ohio Univ. (United States)
Jennifer J. Whitestone, Air Force Armstrong Lab. (United States)
Dennis B. Burnsides, Sytronics, Inc. (United States)
David M. Hoeferlin, Sytronics, Inc. (United States)


Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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