1995•Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

Issues for data reduction of dense three-dimensional data

Joseph H. Nurre, Jennifer J. Whitestone, Dennis B. Burnsides, David M. Hoeferlin

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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.

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What this paper is about

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.

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Available 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.

Key concepts: Data reduction, Reduction (mathematics), Computer science, Dimensional reduction, Data mining, Tracking (education), Algorithm, Mathematics

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