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The Visual Computer

, Volume 32, Issue 4, pp 465–478 | Cite as

Visual inspection of multivariate volume data based on multi-class noise sampling

  • Zhiyu Ding
  • Ziang Ding
  • Weifeng Chen
  • Haidong Chen
  • Yubo Tao
  • Xin Li
  • Wei Chen
Original Article

Abstract

Visualizing multivariate volume data is useful when the user wants to inspect the correlational distributions of multiple variables in a spatial field. Existing solutions commonly rely on color blending or weaving techniques to show multiple variables on a sampling point, probably causing heavy visual confusion. This paper presents an alternative solution that employs a multi-class sampling technique to generate spatially separated sampling points for multiple variables and illustrates the sampling points of each variable individually. We combine this new sampling scheme with the conventional direct volume rendering mode, iso-surface mode, and the cutting plane mode to support interactive inspection of volumetric distributions of multiple variables. The effectiveness of our approach is demonstrated with the IEEE VIS Contest 2004 Hurricane dataset and a 3D nuclear fusion simulation dataset.

Keywords

Multivariate volume Volume visualization Blue noise Sampling 

Notes

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments. This work was partially supported by the National High Technology Research and Development Program of China (2012AA12090), Major Program of National Natural Science Foundation of China (61232012), National Natural Science Foundation of China (61422211), National Natural Science Foundation of China (61303134), the Fundamental Research Funds for the Central Universities (2013QNA5010).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Zhiyu Ding
    • 1
  • Ziang Ding
    • 2
  • Weifeng Chen
    • 3
  • Haidong Chen
    • 1
  • Yubo Tao
    • 1
  • Xin Li
    • 4
  • Wei Chen
    • 1
  1. 1.State Key Lab of CAD&CGZhejiang UniversityHangzhouChina
  2. 2.Department of Computer SciencePurdue UniversityWest LafayetteUSA
  3. 3.Zhejiang University of Finance and EconomicsHangzhouChina
  4. 4.China University of PetroleumBeijingChina

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