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Proceedings Paper

Visual identification of structure in four-dimensional data
Author(s): Robert R. Johnson; Rodney Millar; Isaac Ben
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Paper Abstract

Structure in 4-D data is visualized with a new modeling algorithm called SBP. The SBP vector fusion algorithm makes 3-D display space models of data having any dimensionality that is input in matrix form. SBP maps points on complete manifolds in 4-D to 3-D to visualize any 4-D data. Starting with familiar shapes in 2-D data, 3-D models are constructed to demonstrate how SBP works. Then 3-D data is modeled in 3-D display space. Finally 4-D data are modeled in 3-D display space. The 3-D display space models are points mapped from collections of points on 4-D manifolds. Two types of SBP models are discussed: the latitude/longitude collection and the helical collection. SBP also maps points on complete manifolds of n-D data to 3-D display space models. The objective of this work is to present what 4-D spheres and tori look like when visualized from 4-D data using the SBP algorithm. This demonstrates the SBP algorithm as a new and useful tool for visualizing and understanding 4-D data, and by implication, n-D geometry. Future uses for SBP could be modeling and studying protein structure and space-time structure in general relativity and string theory.

Paper Details

Date Published: 9 June 2003
PDF: 12 pages
Proc. SPIE 5009, Visualization and Data Analysis 2003, (9 June 2003); doi: 10.1117/12.473902
Show Author Affiliations
Robert R. Johnson, n-Dimensional Visualization LLC (United States)
Rodney Millar, n-Dimensional Visualization LLC (United States)
Isaac Ben, n-Dimensional Visualization LLC (United States)


Published in SPIE Proceedings Vol. 5009:
Visualization and Data Analysis 2003
Robert F. Erbacher; Philip C. Chen; Jonathan C. Roberts; Matti T. Groehn; Katy Boerner, Editor(s)

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