Group dynamics in scientific visualization

Sedat Ozer, Jishang Wei, Deborah Silver, Kwan-Liu Ma, Pino Martin

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

13 Scopus citations


The ability to visually extract and track features is appealing to scientists in many simulations including flow fields. However, as the resolution of the simulation becomes higher, the number of features to track increases and so does the cost in large-scale simulations. Since many of these features act in groups, it seems more cost-effective to follow groups of features rather than individual ones. Very little work has been done for tracking groups of features. In this paper, we present the first full group tracking framework in which we track groups (clusters) of features in time-varying 3D fluid flow simulations. Our framework uses a clustering algorithm to group interacting features. We demonstrate the use of our framework on data output from a 3D simulation of wall bounded turbulent flow.

Original languageEnglish (US)
Title of host publicationIEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings
Number of pages8
StatePublished - Dec 1 2012
Event2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012 - Seattle, WA, United States
Duration: Oct 14 2012Oct 19 2012


Other2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012
Country/TerritoryUnited States
CitySeattle, WA


  • clustering
  • Feature tracking
  • group tracking
  • grouping
  • packet identification
  • scientific visualization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems


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