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Adaptive Distance Grid Based Algorithm for Farthest Point Seeding Streamline Placement

Adaptive Distance Grid Based Algorithm for Farthest Point Seeding Streamline Placement AbstractStreamlines are commonly used in scientific visualization.They are the most used geometric items inprimitive-based visualization algorithms. In this paper, amodified version of the farthest point seeding strategystreamline placement is presented. The main advantagecompared to the original method is the use of a simple easyto implement underlying data structure. The Delaunay triangulationused in the original algorithm is heavy and susceptibleto the calculation robustness errors. The distancesbetween lines and the localization of the biggest void inthe domain are approximated using the Delaunay triangulation.This paper presents an adaptive distance gridto model the visualization domain and incorporate anywherethe local distance to all the other streamlines andthe boundaries exactly without any approximation. Thestreamlines are started at the farthest point from all the existingones and the domain boundaries. The localizationof the seed point position is directly accessible via the distanceadaptive grid. The grid update is direct too, and isdone by a greedy algorithm avoiding any additional cost.The obtained results are sufficient, and the extension tomulti-resolution is straight and simple. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Computer Science de Gruyter

Adaptive Distance Grid Based Algorithm for Farthest Point Seeding Streamline Placement

Open Computer Science , Volume 6 (1): 9 – Jan 1, 2016

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Publisher
de Gruyter
Copyright
© 2016 Abdelkrim Mebarki
eISSN
2299-1093
DOI
10.1515/comp-2016-0007
Publisher site
See Article on Publisher Site

Abstract

AbstractStreamlines are commonly used in scientific visualization.They are the most used geometric items inprimitive-based visualization algorithms. In this paper, amodified version of the farthest point seeding strategystreamline placement is presented. The main advantagecompared to the original method is the use of a simple easyto implement underlying data structure. The Delaunay triangulationused in the original algorithm is heavy and susceptibleto the calculation robustness errors. The distancesbetween lines and the localization of the biggest void inthe domain are approximated using the Delaunay triangulation.This paper presents an adaptive distance gridto model the visualization domain and incorporate anywherethe local distance to all the other streamlines andthe boundaries exactly without any approximation. Thestreamlines are started at the farthest point from all the existingones and the domain boundaries. The localizationof the seed point position is directly accessible via the distanceadaptive grid. The grid update is direct too, and isdone by a greedy algorithm avoiding any additional cost.The obtained results are sufficient, and the extension tomulti-resolution is straight and simple.

Journal

Open Computer Sciencede Gruyter

Published: Jan 1, 2016

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