Name
Title | ||
|---|---|---|
Surface extraction from multi-field particle volume data using multi-dimensional cluster visualization. |
Abstract | ||
|---|---|---|
Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the entire multi-field volume data rather than concentrating on one variable. We present a visualization approach based on surface extraction from multi-field particle volume data. The surfaces segment the data with respect to the underlying multi-variate function. Decisions on segmentation properties are based on the analysis of the multi-dimensional feature space. The feature space exploration is performed by an automated multi-dimensional hierarchical clustering method, whose resulting density clusters are shown in the form of density level sets in a 3D star coordinate layout. In the star coordinate layout, the user can select clusters of interest. A selected cluster in feature space corresponds to a segmenting surface in object space. Based on the segmentation property induced by the cluster membership, we extract a surface from the volume data. Our driving applications are Smoothed Particle Hydrodynamics (SPH) simulations, where each particle carries multiple properties. The data sets are given in the form of unstructured point-based volume data. We directly extract our surfaces from such data without prior resampling or grid generation. The surface extraction computes individual points on the surface, which is supported by an efficient neighborhood computation. The extracted surface points are rendered using point-based rendering operations. Our approach combines methods in scientific visualization for object-space operations with methods in information visualization for feature-space operations. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TVCG.2008.167 | IEEE Trans. Vis. Comput. Graph. |
Keywords | Field | DocType |
surface point,segmentation property,unstructured point-based volume data,point-based visualization,star coordi- nates,index termsmulti-�eld and multi-variate visualization,feature space corresponds,multi-field particle volume data,multi-dimensional cluster visualization,volume data,entire multi-field volume data,particle simulations.,surface extraction,isosurfaces and surface extraction,feature space exploration,visualization in astrophysics,data visualisation,astrophysics,density functional theory,grid generation,feature space,data mining,interpolation,smooth particle hydrodynamics,indexing terms,feature extraction,hierarchical clustering,information visualization,scientific visualization | Computer vision,Feature vector,Data set,Data visualization,Information visualization,Visualization,Computer science,Feature extraction,Theoretical computer science,Artificial intelligence,Rendering (computer graphics),Scientific visualization | Journal |
Volume | Issue | ISSN |
14 | 6 | 1077-2626 |
Citations | PageRank | References |
13 | 0.82 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lars Linsen | 1 | 280 | 45.80 |
| Tran Van Long | 2 | 41 | 3.28 |
| Paul Rosenthal | 3 | 70 | 10.03 |
| Stephan Rosswog | 4 | 14 | 1.53 |