Automatic Detection and Visualization of Distinctive Structures in 3D Unsteady Multi-fields

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dc.contributor.authorJänicke, Heikeen_US
dc.contributor.authorBöttinger, Michaelen_US
dc.contributor.authorTricoche, Xavieren_US
dc.contributor.authorScheuermann, Geriken_US
dc.contributor.editorA. Vilanova, A. Telea, G. Scheuermann, and T. Moelleren_US
dc.date.accessioned2014-02-21T18:44:57Z
dc.date.available2014-02-21T18:44:57Z
dc.date.issued2008en_US
dc.description.abstractCurrent unsteady multi-field simulation data-sets consist of millions of data-points. To efficiently reduce this enormous amount of information, local statistical complexity was recently introduced as a method that identifies distinctive structures using concepts from information theory. Due to high computational costs this method was so far limited to 2D data. In this paper we propose a new strategy for the computation that is substantially faster and allows for a more precise analysis. The bottleneck of the original method is the division of spatio-temporal configurations in the field (light-cones) into different classes of behavior. The new algorithm uses a density-driven Voronoi tessellation for this task that more accurately captures the distribution of configurations in the sparsely sampled high-dimensional space. The efficient computation is achieved using structures and algorithms from graph theory. The ability of the method to detect distinctive regions in 3D is illustrated using flow and weather simulations.en_US
dc.description.number3en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume27en_US
dc.identifier.doi10.1111/j.1467-8659.2008.01206.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2008.01206.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleAutomatic Detection and Visualization of Distinctive Structures in 3D Unsteady Multi-fieldsen_US
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EuroVis08: Joint Eurographics - IEEE VGTC Symposium on Visualization