Gaussian mixture model based volume visualization

Shusen Liu, Joshua A. Levine, Peer Timo Bremer, Valerio Pascucci

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Scopus citations

Abstract

Representing uncertainty when creating visualizations is becoming more indispensable to understand and analyze scientific data. Uncertainty may come from different sources, such as, ensembles of experiments or unavoidable information loss when performing data reduction. One natural model to represent uncertainty is to assume that each position in space instead of a single value may take on a distribution of values. In this paper we present a new volume rendering method using per voxel Gaussian mixture models (GMMs) as the input data representation. GMMs are an elegant and compact way to drastically reduce the amount of data stored while still enabling realtime data access and rendering on the GPU. Our renderer offers efficient sampling of the data distribution, generating renderings of the data that flicker at each frame to indicate high variance. We can accumulate samples as well to generate still frames of the data, which preserve additional details in the data as compared to either traditional scalar indicators (such as a mean or a single nearest neighbor down sample) or to fitting the data with only a single Gaussian per voxel. We demonstrate the effectiveness of our method using ensembles of climate simulations and MRI scans as well as the down sampling of large scalar fields as examples.

Original languageEnglish (US)
Title of host publicationIEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings
Pages73-77
Number of pages5
DOIs
StatePublished - 2012
Externally publishedYes
Event2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012 - Seattle, WA, United States
Duration: Oct 14 2012Oct 19 2012

Publication series

NameIEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings

Conference

Conference2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012
Country/TerritoryUnited States
CitySeattle, WA
Period10/14/1210/19/12

Keywords

  • Ensemble Visualization
  • Gaussian Mixture Model
  • Uncertainty Visualization
  • Volume Rendering

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems

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