Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/LDAV.2012.6378982 | Large Data Analysis and Visualization |
Keywords | Field | DocType |
boundary layer turbulence,data visualisation,flow simulation,mechanical engineering computing,pattern clustering,clustering algorithm,feature extraction,feature tracking,flow fields,group dynamics,group tracking framework,scientific visualization,time-varying 3D fluid flow simulations,wall bounded turbulent flow,Feature tracking,clustering,group tracking,grouping,packet identification,scientific visualization | Data visualization,Correlation clustering,Computer science,Flow (psychology),Turbulence,Algorithm,Theoretical computer science,Fluid dynamics,Cluster analysis,Scientific visualization,Bounded function | Conference |
ISBN | Citations | PageRank |
978-1-4673-4732-7 | 7 | 0.53 |
References | Authors | |
18 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sedat Ozer | 1 | 34 | 4.85 |
| Jishang Wei | 2 | 102 | 10.17 |
| Deborah Silver | 3 | 566 | 23.48 |
| Kwan-Liu Ma | 4 | 5145 | 334.46 |
| Pino Martin | 5 | 14 | 0.97 |