Centroidal Voronoi tessellations (CVTs) are special Voronoi diagrams for which the generators of the diagrams are also the centers of mass (with respect to a given density function) of the Voronoi cells. CVTs have many uses and applications, including data compression, image segmentation, clustering, cell biology, territorial behavior of animals, resource allocation, grid generation in volumes and on surfaces, meshless computing, hypercube sampling and reduced-order modeling. We then discuss deterministic and probabilistic methods for determining CVTs, including some new probabilistic methods that are amenable to parallel processing.
Sponsored by the Methods of Advanced Scientific Computation Group (CCS-2), Ken Hanson, host
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