Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/de-gruyter/adaptive-distance-grid-based-algorithm-for-farthest-point-seeding-6E0njL03Dj
References
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
- 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 Science – de Gruyter
Published: Jan 1, 2016