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Outline

Title
Abstract
Introduction
Related Work
Seismic Domain: Data and Uncertainty
Conclusion
References
All Topics
Computer Science
Computer Graphics

Exploration of uncertainty in bidirectional vector fields

Profile image of Sheelagh CarpendaleSheelagh Carpendale

2008, SPIE Proceedings

https://doi.org/10.1117/12.768317
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10 pages

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Abstract

While their importance is increasingly recognized, there remain many challenges in the development of uncertainty visualizations. We introduce two uncertainty visualizations for 2D bidirectional vector fields: one based on a static glyph and the other based on animated flow. These visualizations were designed for the task of understanding and interpreting anisotropic rock property models in the domain of seismic data processing. Aspects of the implementations are discussed relating to design, interaction, and tasks.

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References (27)

  1. R. Amar and J. Stasko. Knowledge precepts for design and evaluation of information visualizations. Visualization and Computer Graphics, IEEE Transactions on, 11(4):432-442, 2005.
  2. M. K. Beard and B. Buttenfield. Detecting and evaluating errors by graphical methods. In P. Longley, M. Goodchild, D. Maguire, and D. Rhind, editors, Geographic Information Systems: Principles and Technical Issues. Vol. 1, pages 219-233. John Wiley, New York, 1999.
  3. R. P. Botchen, D. Weiskopf, and T. Ertl. Texture-based visualization of uncertainty in flow fields. In Visualization, 2005. VIS 05. IEEE, pages 647-654, 2005.
  4. K. Bürger, J. Schneider, P. Kondratieva, J. Krüger, and R. Westermann. Interactive visual exploration of unsteady 3d flows. In Proceedings of Eurographics / IEEE VGTC Symposium on Visualization (EuroVis 2007, May 23-25, 2007, Norrköping, Sweden), Eurographics Workshop Series, Aire-la-Ville, 2007. Eurographics.
  5. J. Downton and D. Gray. AVAZ parameter uncertainty estimation. In 76th Annual International Meeting, SEG, Expanded Abstracts, pages 234-238, 2006.
  6. R. M. Ford. Critical point detection in fluid flow images using dynamical system properties. Pattern Recognition, 30(12):1991-2000, 1997.
  7. G. Grigoryan and P. Rheingans. Point-based probabilistic surfaces to show surface uncertainty. IEEE Transactions on Visualization and Computer Graphics, 10(5):564-573, 2004.
  8. B. Jobard, G. Erlebacher, and M. Y. Hussaini. Lagrangian-eulerian advection of noise and dye textures for unsteady flow visualization. IEEE Transactions on Visualization and Computer Graphics, 8(3):211-222, JUL/SEP 2002.
  9. C. Johnson. Top scientific visualization research problems. IEEE Computer Graphics and Applications, 24(4):13-17, July/August 2004.
  10. D. Laidlaw, R. Kirby, C. Jackson, J. Davidson, T. Miller, M. da Silva, W. Warren, and M. Tarr. Comparing 2d vector field visualization methods: a user study. Visualization and Computer Graphics, IEEE Transactions on, 11(1):59-70, Jan/Feb 2005.
  11. S. K. Lodha, A. Pang, R. E. Sheehan, and C. M. Wittenbrink. UFLOW: Visualizing uncertainty in fluid flow. In R. Yagel and G. M. Nielson, editors, IEEE Visualization '96,, pages 249-254. IEEE, 1996.
  12. A. L. Love, A. Pang, and D. Kao. Visualizing spatial multivalue data. IEEE Computer Graphics and Applications, 25(3):69-79, May/June 2005.
  13. A. M. MacEachren. Visualizing uncertain information. Cartographic Perspective, 13:10-19, 1992.
  14. A. M. MacEachren, A. Robinson, S. Hopper, S. Gardner, and R. Murray. Visualizing geospatial information uncer- tainty: What we know and what we need to know. Cartography and Geographic Information Science, 32(3):139-160, 2005.
  15. A. T. Pang, C. M. Wittenbrink, and S. K. Lodha. Approaches to uncertainty visualization. The Visual Computer, 13(8):370-390, 1997. ISSN 0178-2789.
  16. P. Rheingans and S. Joshi. Visualization of molecules with positional uncertainty. In E. Gröller, H. Löffelmann, and W. Ribarsky, editors, Data Visualization '99, pages 299-306. Springer-Verlag Wien, 1999.
  17. A. R. Sanderson, C. R. Johnson, and R. M. Kirby. Display of vector fields using a reaction-diffusion model. In Visualization, 2004. IEEE, pages 115-122, 2004.
  18. A. Telea and R. Strzodka. Multiscale image based flow visualization. In R. F. Erbacher, J. C. Roberts, M. T. Gröhn, and K. Börner, editors, Proc. SPIE & IS&T Conf. Electronic Imaging, Vol. 6060: Visualization and Data Analysis 2006, 2006. 606001.
  19. J. Thomson, E. Hetzler, A. MacEachren, M. Gahegan, and M. Pavel. A typology for visualizing uncertainty. In Proc. SPIE & IS&T Conf. Electronic Imaging, Vol. 5669: Visualization and Data Analysis 2005, pages 146-157, 2005.
  20. E. R. Tufte. The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT, 2nd edition, 2001.
  21. J. J. van Wijk. Image based flow visualization. ACM Transactions on Graphics (TOG), 21(3):745-754, 2002.
  22. C. Ware. Information Visualization: Perception for Design. Morgan Kaufmann Publishers, 2nd edition, 2004.
  23. C. M. Wittenbrink, A. T. Pang, and S. K. Lodha. Glyphs for visualizing uncertainty in vector fields. IEEE Transactions on Visualization and Computer Graphics, 2(3):266-279, 1996.
  24. T. Zuk and M. S. T. Carpendale. Theoretical analysis of uncertainty visualizations. In R. F. Erbacher, J. C. Roberts, M. T. Gröhn, and K. Börner, editors, Proc. SPIE & IS&T Conf. Electronic Imaging, Vol. 6060: Visualization and Data Analysis 2006, 2006. 606007.
  25. T. Zuk, M. S. T. Carpendale, and W. Glanzman. Visualizing temporal uncertainty in 3D virtual reconstructions. In Proc. of the 6th International Symposium on Virtual Reality, Archaeology and Cultural Heritage (VAST 2005), pages 99-106, 2005.
  26. T. Zuk and S. Carpendale. Visualization of uncertainty and reasoning. In Proceedings of the 7th International Symposium on Smart Graphics (June 25-27, 2007, Kyoto, Japan), Berlin, Heidelberg., 2007.
  27. T. Zuk, L. Schlesier, P. Neumann, M. S. Hancock, and S. Carpendale. Heuristics for information visualization evaluation. In Proceedings of the 2006 AVI workshop on BEyond time and errors: Novel Evaluation Methods for Information Visualization (BELIV 2006), pages 55-60. ACM Press, 2006.

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