Linking Multi-dimensional Feature Space Cluster Visualization to Surface Extraction from Multi-field Volume Data
نویسندگان
چکیده
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 multidimensional 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 induced by the cluster membership, we extract surfaces from the volume data.
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