maximize


@inproceedings{GaPrRuTeWeWi00,
  author = {Garcke, H. and Preu{\ss}er, T. and Rumpf, M. and Telea, A. and Weikard,
	U. and Van Wijk, J.},
  title = {A continuous Clustering Method for vector fields},
  booktitle = {Visualization},
  year = {2000},
  pages = {351--358},
  publisher = {IEEE Computer Society},
  abstract = {A new method for the simplification of flow fields is presented. It
	is based on continuous clustering. A well-known physical clustering
	model, the Cahn Hillard model which describes phase separation, is
	modified to reflect the properties of the data to be visualized.
	Clusters are defined implicitly as connected components of the positivity
	set of a density function. An evolution equation for this function
	is obtained as a suitable gradient flow of an underlying anisotropic
	energy functional. Here, time serves as the scale parameter. The
	evolution is characterized by a successive coarsening of patterns
	--- the actual clustering --- and meanwhile the underlying simulation
	data specifies preferable pattern boundaries. Here we discuss the
	applicability of this new type of approach mainly for flow fields,
	where the cluster energy penalizes cross streamline boundaries, but
	the method also carries provisions in other fields as well. The clusters
	are visualized via iconic representations. A skeletonization algorithm
	is used to find suitable positions for the icons.},
  pdf = {http://numod.ins.uni-bonn.de/research/papers/public/GaPrRuTeWeWi00.pdf 1},
  printed = {1}
}