Group dynamics in scientific visualization

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 feature...

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Bibliographic Details
Main Authors: Ozer, S., Jishang Wei, Silver, D., Kwan-Liu Ma, Martin, P.
Format: Conference Proceeding
Language:English
Subjects:
clustering
Clustering algorithms
Data visualization
Feature extraction
Feature tracking
group tracking
grouping
Image color analysis
packet identification
Radar tracking
scientific visualization
Shape
Tracking
Online Access:Request full text
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Summary: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.
DOI:10.1109/LDAV.2012.6378982