Glyph-Based Multi-field Visualization

  • David H.S. ChungEmail author
  • Robert S. Laramee
  • Johannes Kehrer
  • Helwig Hauser
Part of the Mathematics and Visualization book series (MATHVISUAL)


In this chapter, we present a state of the art on glyph-based visualization techniques that address the complex challenges of multi-field visualization. Glyphs are discrete parametrized visualization objects that encode multiple data values based on appearance (i.e., visual channels) such as size, shape, color, and opacity, and are effective for conveying multiple fields of data simultaneously. We provide a categorization of these techniques with the aim for an informative overview of recent literature. Our categorization is based on visual channels utilized by the glyph for mapping each data attribute, and the spatial dimensionality of the glyph-based visualization. We also discuss critical design aspects of glyph-based visualization to deal with the perceptual challenges inherent with this approach.


Depth Perception Mesh Resolution Visual Channel Visual Clutter Spatial Dimensionality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Barr, A.H.: Superquadrics and angle-preserving transformations. IEEE Comput. Graph. Appl. 1(1), 11–23 (1981)CrossRefGoogle Scholar
  2. 2.
    Chlan, E.B., Rheingans, P.: Multivariate glyphs for multi-object clusters. In: INFOVIS, p. 19 (2005)Google Scholar
  3. 3.
    Crawfis, R., Allison, M.J.: A scientific visualization synthesizer. In: Proceedings of the IEEE visualization, pp. 262–267 (1991)Google Scholar
  4. 4.
    Hauser, H., Schumann, H.: Visualization pipeline. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of database systems, pp. 3414–3416. Springer, Berlin (2009)Google Scholar
  5. 5.
    Healey, C., Booth, K., Enns, J.: High-speed visual estimation using preattentive processing. ACM Trans. Comput.-Human Interact. 3, 107–135 (1996)CrossRefGoogle Scholar
  6. 6.
    Healey, C.G., Enns, J.T.: Large datasets at a glance: combining textures and colors in scientific visualization. IEEE Trans. Vis. Comput. Graph. 5(2), 145–167 (1999)CrossRefGoogle Scholar
  7. 7.
    Jankun-Kelly, T., Mehta, K.: Superellipsoid-based, real symmetric traceless tensor glyphs motivated by nematic liquid crystal alignment visualization. IEEE Trans. Vis. Comput. Graph. 12(5), 1197–1204 (2006)CrossRefGoogle Scholar
  8. 8.
    Kehrer, J., Muigg, P., Doleisch, H., Hauser, H.: Interactive visual analysis of heterogeneous scientific data across an interface. IEEE Trans. Vis. Comput. Graph. 17(7), 934–946 (2011)CrossRefGoogle Scholar
  9. 9.
    Kindlmann, G.: Superquadric tensor glyphs. In: Deussen, O., Hansen, C., Keim, D., Saupe, D. (eds.) Joint Eurographics—IEEE TCVG symposium on visualization, Konstanz, Germany, pp. 147–154 (2004)Google Scholar
  10. 10.
    Kindlmann, G., Westin, C.F.: Diffusion tensor visualization with glyph packing. IEEE Trans. Vis. Comput. Graph. 12(5), 1329–1336 (2006)CrossRefGoogle Scholar
  11. 11.
    Kirby, R.M., Marmanis, H., Laidlaw, D.H.: Visualizing multivalued data from 2D incompressible flows using concepts from painting. In: Proceedings of the IEEE visualization, pp. 333–340 (1999)Google Scholar
  12. 12.
    Kraus, M., Ertl, T.: Interactive data exploration with customized glyphs. In: WSCG (Posters), pp. 20–23 (2001)Google Scholar
  13. 13.
    de Leeuw, W.C., van Wijk, J.J.: A probe for local flow field visualization. In: Proceedings of the IEEE visualization conference (Vis’93), pp. 39–45 (1993)Google Scholar
  14. 14.
    Lie, A.E., Kehrer, J., Hauser, H.: Critical design and realization aspects of glyph-based 3D data visualization. In: Proceedings of the spring conference on computer graphics (SCCG 2009), pp. 27–34 (2009)Google Scholar
  15. 15.
    Meyer-Spradow, J., Stegger, L., Döring, C., Ropinski, T., Hinrichs, K.: Glyph-based SPECT visualization for the diagnosis of coronary artery disease. IEEE Trans. Vis. Comput. Graph. 14(6), 1499–1506 (2008)CrossRefGoogle Scholar
  16. 16.
    Peng, Z., Grundy, E., Laramee, R., Chen, G., Croft, N.: Mesh-driven vector field clustering and visualization: an image-based approach. IEEE Trans. Vis. Comput. Graph. (TVCG), 283–298 (2011)Google Scholar
  17. 17.
    Pickett, R.M., Grinstein, G.G.: Iconographic displays for visualizing multidimensional data. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 1, pp. 514–519 (1988)Google Scholar
  18. 18.
    Piringer, H., Kosara, R., Hauser, H.: Interactive focus+context visualization with linked 2D/3D scatterplots. In: Proceedings on coordinated and multiple views in exploratory visualization (CMV 2004), pp. 49–60 (2004)Google Scholar
  19. 19.
    Ropinski, T., Preim, B.: Taxonomy and usage guidelines for glyph-based medical visualization. In: Proceedings on simulation and visualization, pp. 121–138 (2008)Google Scholar
  20. 20.
    Sanyal, J., Zhang, S., Dyer, J., Mercer, A., Amburn, P., Moorhead, R.: Noodles: a tool for visualization of numerical weather model ensemble uncertainty. IEEE Trans. Vis. Comput. Graph. 16(6), 1421–1430 (2010)CrossRefGoogle Scholar
  21. 21.
    Shaw, C.D., Ebert, D.S., Kukla, J.M., Zwa, A., Soboroff, I., Roberts, D.A.: Data visualization using automatic, perceptually-motivated shapes. In: SPIE 3298, visual data exploration and analysis (1998)Google Scholar
  22. 22.
    Taylor, R.: Visualizing multiple fields on the same surface. IEEE Comput. Graph. Appl. 22(3), 6–10 (2002)CrossRefGoogle Scholar
  23. 23.
    Toutin, T.: Qualitative aspects of chromo-stereoscopy for depth-perception. Photogram. Eng. Remote Sens. 63(2), 193–203 (1997)Google Scholar
  24. 24.
    Ward, M.O.: A taxonomy of glyph placement strategies for multidimensional data visualization. Info. Vis. 1(3–4), 194–210 (2002)CrossRefGoogle Scholar
  25. 25.
    Wittenbrink, C.M., Pang, A., Lodha, S.K.: Glyphs for visualizing uncertainty in vector fields. IEEE Trans. Vis. Comput. Graph. 2(3), 266–279 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • David H.S. Chung
    • 1
    Email author
  • Robert S. Laramee
    • 1
  • Johannes Kehrer
    • 2
  • Helwig Hauser
    • 2
  1. 1.Swansea UniversitySwanseaUK
  2. 2.University of BergenBergenNorway

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