Bjoern Heckel

Surface Reconstruction Using Adaptive Clustering Methods

We present an automatic method for the generation of surface triangulations from sets of scattered points. Given a set of scattered points in three-dimensional space, without connectivity information, our method reconstructs a triangulated surface model in a two-step procedure. First, we apply an adaptive clustering technique to the given set of points, identifying point subsets in regions that are nearly planar. The output of this clustering step is a set of two-manifold “tiles” that locally approximate the underlying, unknown surface. Second, we construct a surface triangulation by triangulating the data within the individual tiles and the gaps between the tiles. This algorithm can generate multiresolution representations by applying the triangulation step to various resolution levels resulting from the hierarchical clustering step. We compute deviation measures for each cluster, and thus we can produce reconstructions with prescribed error bounds.

EmVis—a visual e-mail analysis tool

Author(s): Heckel, Bjoern; Hamann, Bernd | Editor(s): Ebert, David S.; Nicholas, C. K. | Abstract: EmVis is an information visualization tool for the analysis of e-mail exchange among people in an organizaiton. A company-specific organizational hierarchy is used to provide the basic layout for a graph-based visualization of e-mail exchange. Various parameters are mapped to the nodes and edges in this graph: rank within the company, frequency/amount of e-mail exchange between subjects, possibly information content. The system we describe also supports more general graph-based visualizations not necessarily based on a companys hierarchy; it is possible to use EmVis for queries regarding any group of employees and the e-mail exchange among them. We employ geralizations of traditional information visualization concepts, such as cone tree and hyperbolic space representations

Divisive Parallel Clustering for Multiresolution Analysis

Clustering is a classical data analysis technique that is applied to a wide range of applications in the sciences and engineering. For very large data sets, the performance of a clustering algorithm becomes critical. Although clustering has been thoroughly studied over the last decades, little has been done on utilizing modern multi-processor machines to accelerate the analysis process. We propose a scalable clustering technique that benefits from existing parallel computers and networks of workstations. It enables the creation of multiresolution representations for very large geometric data sets. The output of the clustering process can be used for interactive data exploration, supporting techniques like view-dependent rendering, user-guided refinement, or progressive transmission.