(2008 IEEE Visualization Design Contest Winner) Linking Multi-dimensional Feature Space Cluster Visualization to Surface Extraction from Multi-field Volume Data
Lars Linsen, Tran Van Long, Paul Rosenthal · 2008
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 volume data. The extracted surfaces segment the data with respect to an underlying multi-variate function. Decisions on segmentation properties are based on the analysis of a multi-dimensional feature space. The feature space exploration is performed using an automated multi-dimensional hierarchical clustering method. The hierarchical clusters are shown as a cluster tree in a 2D radial layout. In the cluster tree 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