Sets of Globally Optimal Stream Surfaces for Flow Visualization
Stream surfaces are a well‐studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in time‐consuming trial and error procedures. Only recently automatic selection methods were proposed. Local methods support the selection of a set...
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Published in | Computer graphics forum Vol. 33; no. 3; pp. 1 - 10 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Oxford
Blackwell Publishing Ltd
01.06.2014
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Subjects | |
Online Access | Get full text |
ISSN | 0167-7055 1467-8659 1467-8659 |
DOI | 10.1111/cgf.12356 |
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Summary: | Stream surfaces are a well‐studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in time‐consuming trial and error procedures. Only recently automatic selection methods were proposed. Local methods support the selection of a set of stream surfaces, but, contrary to global selection methods, they evaluate only the quality of the seeding lines but not the quality of the whole stream surfaces. Global methods, on the other hand, only support the selection of a single optimal stream surface until now. However, for certain flow fields a single stream surface is not sufficient to represent all flow features.
In our work, we overcome this limitation by introducing a global selection technique for a set of stream surfaces. All selected surfaces optimize global stream surface quality measures and are guaranteed to be mutually distant, such that they can convey different flow features. Our approach is an efficient extension of the most recent global selection method for single stream surfaces. We illustrate its effectiveness on a number of analytical and simulated flow fields and analyze the quality of the results in a user study. |
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Bibliography: | ark:/67375/WNG-JS2QZN7F-6 Supporting InformationSupporting InformationSupporting Information istex:096DFEC164E212FB212331AEE12E21A60027A232 ArticleID:CGF12356 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0167-7055 1467-8659 1467-8659 |
DOI: | 10.1111/cgf.12356 |