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Feature-based visualization of dense integral line data

 
: Schröder, S.; Obermaier, H.; Garth, C.; Joy, K.I.

:

Garth, C.; Middel, A.; Hagen, H. ; Deutsche Forschungsgemeinschaft -DFG-, International Research and Training Group 1131 "Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering":
Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering. Online Resource : Proceedings of IRTG 1131 Workshop 2011
Wadern: Schloss Dagstuhl, Leibniz-Zentrum für Informatik, 2012 (OASIcs - OpenAccess Series in Informatics 27)
http://www.dagstuhl.de/dagpub/978-3-939897-46-0
ISBN: 978-3-939897-46-0
pp.71-87
Workshop on Visualization of Large and Unstructured Data Sets (VLUDS) <2011, Kaiserslautern>
English
Conference Paper
Fraunhofer ITWM ()

Abstract
Feature-based visualization of flow fields has proven as an effective tool for flow analysis. While most flow visualization techniques operate on vector field data, our visualization techniques make use of a different simulation output: Particle Tracers. Our approach solely relies on integral lines that can be easily obtained from most simulation software. The task is the visualization of dense integral line data. We combine existing methods for streamline visualization, i. e. illumination, transparency, and halos, and add ambient occlusion for lines. But, this only solves one part of the problem: because of the high density of lines, visualization has to fight with occlusion, high frequency noise, and overlaps. As a solution we propose non-automated choices of transfer functions on curve properties that help highlighting important flow features like vortices or turbulent areas. These curve properties resemble some of the original flow properties. With the new combinati on of existing line drawing methods and the addition of ambient occlusion we improve the visualization of lines by adding better shape and depth cues. The intelligent use of transfer functions on curve properties reduces visual clutter and helps focusing on important features while still retaining context, as demonstrated in the examples given in this work.

: http://publica.fraunhofer.de/documents/N-254064.html