Hierarchical Clustering for Unstructured Volumetric Scalar Fields

We present a method to represent unstructured scalar fields at multiple levels of detail. Using a parallelizable classification algorithm to build a cluster hierarchy, we generate a multiresolution representation of a given volumetric scalar data set. The method uses principal component analysis (PCA) for cluster generation and a fitting technique based on radial basis functions (RBFs). Once the cluster hierarchy has been generated, we utilize a variety of techniques for extracting different levels of detail. The main strength of this work is its generality. Regardless of grid type, this method can be applied to any discrete scalar field representation, even one given as a "point cloud." CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling--Curve, surface, solid and object representation I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling--Object hierarchies I.3.6 [Computer Graphics]: Methodology and Techniques--Graphics data...

• Type: Conference
• Year: 2003
• Where: VIS
• Authors: Christopher S. Co, Bjørn Heckel, Hans Hagen, Bernd Hamann, Kenneth I. Joy
• Keywords: Scalar Field Representation, Unstructured Scalar Fields, VIS 2003, Visualization, Volumetric Scalar Data