Interactive visualization of function fields by range-space segmentation

Authors:
John C. Anderson;Luke J. Gosink;Mark A. Duchaineau;Kenneth I. Joy
Affiliations:
Institute for Data Analysis and Visualization, Department of Computer Science, University of California, Davis;Institute for Data Analysis and Visualization, Department of Computer Science, University of California, Davis;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory;Institute for Data Analysis and Visualization, Department of Computer Science, University of California, Davis
Venue:
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Year:
2009

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Abstract

We present a dimension reduction and feature extraction method for the visualization and analysis of function field data. Function fields are a class of high-dimensional, multi-variate data in which data samples are onedimensional scalar functions. Our approach focuses upon the creation of high-dimensional range-space segmentations, from which we can generate meaningful visualizations and extract separating surfaces between features. We demonstrate our approach on high-dimensional spectral imagery, and particulate pollution data from air quality simulations.