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.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings |
| Pages | 97-104 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Dec 1 2012 |
| Event | 2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012 - Seattle, WA, United States Duration: Oct 14 2012 → Oct 19 2012 |
Other
| Other | 2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012 |
|---|---|
| Country | United States |
| City | Seattle, WA |
| Period | 10/14/12 → 10/19/12 |
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Keywords
- clustering
- Feature tracking
- group tracking
- grouping
- packet identification
- scientific visualization
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Information Systems
Cite this
Group dynamics in scientific visualization. / Ozer, Sedat; Wei, Jishang; Silver, Deborah; Ma, Kwan-Liu; Martin, Pino.
IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings. 2012. p. 97-104 6378982.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Group dynamics in scientific visualization
AU - Ozer, Sedat
AU - Wei, Jishang
AU - Silver, Deborah
AU - Ma, Kwan-Liu
AU - Martin, Pino
PY - 2012/12/1
Y1 - 2012/12/1
N2 - 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.
AB - 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.
KW - clustering
KW - Feature tracking
KW - group tracking
KW - grouping
KW - packet identification
KW - scientific visualization
UR - http://www.scopus.com/inward/record.url?scp=84872202460&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872202460&partnerID=8YFLogxK
U2 - 10.1109/LDAV.2012.6378982
DO - 10.1109/LDAV.2012.6378982
M3 - Conference contribution
SN - 9781467347334
SP - 97
EP - 104
BT - IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings
ER -