Skip to main content

Coherent Topological Landscapes for Simulation Ensembles

  • Conference paper
  • First Online:
Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1474))

Abstract

The topological structure is an intrinsic feature of a scalar field of any spatial dimensionality. The dependence of the topology on the isovalue of the field can be represented in the form of merge and split trees, which are usually combined to a contour tree. Topological landscapes are algorithmically constructed 2D scalar fields, which have the same topological structure (and, therefore, correspond to the same contour tree) as the given multidimensional scalar field and serve as an intuitive low-dimensional depiction of its topological features. Topological landscapes computed for a set of scalar fields, e.g., created by varying over time or by varying simulation parameter values in a simulation ensemble, are not necessarily coherent among themselves. Therefore, a comparative analysis of topology in an ensemble is hindered. We propose a concept for the generation of coherent contour trees for simulation ensembles that is based on merging contour trees of all scalar fields of the ensemble. The coherent contour tree can be exploited to generate coherent topological landscapes. Visual analysis of varying scalar field topology is, then, supported by animating landscapes or by volume rendering of a stack of temporal slices representing color-coded landscapes. We apply the proposed methodology to synthetic data for evaluation purposes as well as to 2D and 3D simulation ensemble data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Beketayev, K., Weber, G.H., Morozov, D., Abzhanov, A., Hamann, B.: Geometry-preserving topological landscapes. In: Proceedings of the Workshop at SIGGRAPH Asia, pp. 155–160 (2012). https://doi.org/10.1145/2425296.2425324

  2. Berger, W., Piringer, H., Filzmoser, P., Gröller, E.: Uncertainty-aware exploration of continuous parameter spaces using multivariate prediction. In: Computer Graphics Forum, vol. 30, no. 3, pp. 911–920 (2011). https://doi.org/10.1111/j.1467-8659.2011.01940.x

  3. Bremer, P., Weber, G., Pascucci, V., Day, M., Bell, J.: Analyzing and tracking burning structures in lean premixed hydrogen flames. IEEE Trans. Vis. Comput. Graph. 16(2), 248–260 (2010). https://doi.org/10.1109/TVCG.2009.69

    Article  Google Scholar 

  4. Bruckner, S., Möller, T.: Isosurface similarity maps. In: Computer Graphics Forum, vol. 29, no. 3, pp. 773–782 (2010). https://doi.org/10.1111/j.1467-8659.2009.01689.x

  5. Bruckner, S., Möller, T.: Result-driven exploration of simulation parameter spaces for visual effects design. IEEE Trans. Vis. Comput. Graph. 16(6), 1468–1476 (2010). https://doi.org/10.1109/tvcg.2010.190

    Article  Google Scholar 

  6. Carr, H., Snoeyink, J., Axen, U.: Computing contour trees in all dimensions. Comput. Geom. 24(2), 75–94 (2003). https://doi.org/10.1016/S0925-7721(02)00093-7

    Article  MathSciNet  MATH  Google Scholar 

  7. Demir, D., Beketayev, K., Weber, G.H., Bremer, P.T., Pascucci, V., Hamann, B.: Topology exploration with hierarchical landscapes. In: Proceedings of the Workshop at SIGGRAPH Asia, WASA 2012, pp. 147–154. Association for Computing Machinery, New York (2012). https://doi.org/10.1145/2425296.2425323

  8. Doraiswamy, H., Natarajan, V.: Computing Reeb graphs as a union of contour trees. IEEE Trans. Vis. Comput. Graph. 19(2), 249–262 (2012). https://doi.org/10.1109/TVCG.2012.115

    Article  Google Scholar 

  9. Edelsbrunner, H., Harer, J.: Persistent homology - a survey. Contemp. Math. 453, 257–282 (2008). https://doi.org/10.1090/conm/453/08802

    Article  MathSciNet  MATH  Google Scholar 

  10. Edelsbrunner, H., Harer, J., Mascarenhas, A., Pascucci, V., Snoeyink, J.: Time-varying Reeb graphs for continuous space-time data. Comput. Geom.: Theory Appl. 41(3), 149–166 (2008). https://doi.org/10.1016/j.comgeo.2007.11.001

    Article  MathSciNet  MATH  Google Scholar 

  11. Fofonov, A., Linsen, L.: Fast and robust isosurface similarity maps extraction using Quasi-Monte Carlo approach. In: Wilhelm, A.F.X., Kestler, H.A. (eds.) Analysis of Large and Complex Data. SCDAKO, pp. 497–506. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25226-1_42

    Chapter  Google Scholar 

  12. Fofonov, A., Molchanov, V., Linsen, L.: Visual analysis of multi-run spatio-temporal simulations using isocontour similarity for projected views. IEEE Trans. Vis. Comput. Graph. 22(8), 2037–2050 (2016). https://doi.org/10.1109/TVCG.2015.2498554

    Article  Google Scholar 

  13. Günther, D., Salmon, J., Tierny, J.: Mandatory critical points of 2D uncertain scalar fields. In: Proceedings of the 16th Eurographics Conference on Visualization, pp. 31–40. Eurographics Association, July 2014. https://doi.org/10.1111/cgf.12359

  14. Gyulassy, A., Natarajan, V., Pascucci, V., Hamann, B.: Efficient computation of Morse-Smale complexes for three-dimensional scalar functions. IEEE Trans. Vis. Comput. Graph. 13(6), 1440–1447 (2007). https://doi.org/10.1109/TVCG.2007.70552

    Article  Google Scholar 

  15. Hahn, H.K., Peitgen, H.O.: IWT-interactive watershed transform: a hierarchical method for efficient interactive and automated segmentation of multidimensional grayscale images. In: Proceedings of the SPIE Medical Imaging: Image Processing, vol. 5032, pp. 643–653 (2003). https://doi.org/10.1117/12.481097

  16. Harvey, W., Wang, Y.: Topological landscape ensembles for visualization of scalar-valued functions. In: Computer Graphics Forum, vol. 29, no. 3, pp. 993–1002 (2010). https://doi.org/10.1111/j.1467-8659.2009.01706.x

  17. Heine, C., et al.: A survey of topology-based methods in visualization. In: Computer Graphics Forum, vol. 35, no. 3, pp. 643–667 (2016). https://doi.org/10.1111/cgf.12933

  18. Herick, M., Molchanov, V., Linsen, L.: Temporally coherent topological landscapes for time-varying scalar fields. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, pp. 54–61. INSTICC, SciTePress, January 2020. https://doi.org/10.5220/0008956300540061

  19. Kraus, M.: Visualization of uncertain contour trees. In: IMAGAPP/IVAPP, pp. 132–139 (2010). https://doi.org/10.5220/0002817201320139

  20. van Kreveld, M., van Oostrum, R., Bajaj, C., Pascucci, V., Schikore, D.: Contour trees and small seed sets for isosurface traversal. In: Proceedings of the Thirteenth Annual Symposium on Computational Geometry, SCG 1997, pp. 212–220. Association for Computing Machinery, New York (1997). https://doi.org/10.1145/262839.269238

  21. Lohfink, A.P., Wetzels, F., Lukasczyk, J., Weber, G.H., Garth, C.: Fuzzy contour trees: alignment and joint layout of multiple contour trees. In: Computer Graphics Forum, vol. 39, no. 3, pp. 343–355 (2020). https://doi.org/10.1111/cgf.13985

  22. Milnor, J.: Morse theory. Ann. Math. Stud. 51 (1963)

    Google Scholar 

  23. Oesterling, P., Heine, C., Jänicke, H., Scheuermann, G.: Visual analysis of high dimensional point clouds using topological landscapes. In: 2010 IEEE Pacific Visualization Symposium (PacificVis), pp. 113–120 (2010). https://doi.org/10.1109/PACIFICVIS.2010.5429601

  24. Oesterling, P., Heine, C., Janicke, H., Scheuermann, G., Heyer, G.: Visualization of high-dimensional point clouds using their density distribution’s topology. IEEE Trans. Vis. Comput. Graph. 17(11), 1547–1559 (2011). https://doi.org/10.1109/TVCG.2011.27

    Article  Google Scholar 

  25. Oesterling, P., Heine, C., Weber, G.H., Morozov, D., Scheuermann, G.: Computing and visualizing time-varying merge trees for high-dimensional data. In: Carr, H., Garth, C., Weinkauf, T. (eds.) TopoInVis 2015. MV, pp. 87–101. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44684-4_5

    Chapter  Google Scholar 

  26. Pascucci, V., Cole-McLaughlin, K., Scorzelli, G.: Multi-resolution computation and presentation of contour trees. In: Proceedings of the IASTED Conference on Visualization, Imaging, and Image Processing, pp. 452–290 (2004)

    Google Scholar 

  27. Pfaffelmoser, T., Westermann, R.: Visualizing contour distributions in 2D ensemble data. In: EuroVis (Short Papers), pp. 55–59 (2013)

    Google Scholar 

  28. Phadke, M.N., et al.: Exploring ensemble visualization. In: Visualization and Data Analysis 2012, vol. 8294, p. 82940B. International Society for Optics and Photonics, January 2012. https://doi.org/10.1117/12.912419

  29. Potter, K., et al.: Ensemble-vis: a framework for the statistical visualization of ensemble data. In: 2009 IEEE International Conference on Data Mining Workshops, pp. 233–240. IEEE (2009). https://doi.org/10.1109/ICDMW.2009.55

  30. Reeb, G.: Sur les points singuliers d’une forme de pfaff completement integrable ou d’une fonction numerique [on the singular points of a completely integrable pfaff form or of a numerical function]. Comptes Rendus Acad. Sciences Paris 222, 847–849 (1946)

    MATH  Google Scholar 

  31. Saikia, H., Seidel, H.P., Weinkauf, T.: Extended branch decomposition graphs: structural comparison of scalar data. In: Computer Graphics Forum, vol. 33, no. 3, pp. 41–50 (2014). https://doi.org/10.1111/cgf.12360

  32. 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). https://doi.org/10.1109/TVCG.2010.181

    Article  Google Scholar 

  33. Scheidegger, C.E., Schreiner, J.M., Duffy, B., Carr, H., Silva, C.T.: Revisiting histograms and isosurface statistics. IEEE Trans. Vis. Comput. Graph. 14(6), 1659–1666 (2008). https://doi.org/10.1109/TVCG.2008.160

    Article  Google Scholar 

  34. Schneider, B., Jäckle, D., Stoffel, F., Diehl, A., Fuchs, J., Keim, D.: Visual integration of data and model space in ensemble learning. IEEE (2017). https://doi.org/10.1109/VDS.2017.8573444

  35. Sedlmair, M., Heinzl, C., Bruckner, S., Piringer, H., Möller, T.: Visual parameter space analysis: a conceptual framework. IEEE Trans. Vis. Comput. Graph. 20(12), 2161–2170 (2014). https://doi.org/10.1109/tvcg.2014.2346321

    Article  Google Scholar 

  36. Sohn, B.S., Bajaj, C.: Time-varying contour topology. IEEE Trans. Vis. Comput. Graph. 12(1), 14–25 (2005). https://doi.org/10.1109/TVCG.2006.16

    Article  Google Scholar 

  37. Tierny, J., Favelier, G., Levine, J.A., Gueunet, C., Michaux, M.: The topology ToolKit. IEEE Trans. Vis. Comput. Graph. 24(1), 832–842 (2018). https://doi.org/10.1109/TVCG.2017.2743938

    Article  Google Scholar 

  38. Wang, J., Hazarika, S., Li, C., Shen, H.W.: Visualization and visual analysis of ensemble data: a survey. IEEE Trans. Vis. Comput. Graph. 25(9), 2853–2872 (2019). https://doi.org/10.1109/tvcg.2018.2853721

    Article  Google Scholar 

  39. Weber, G., Bremer, P.T., Day, M., Bell, J., Pascucci, V.: Feature tracking using Reeb graphs. In: Pascucci, V., Tricoche, X., Hagen, H., Tierny, J. (eds.) Topological Methods in Data Analysis and Visualization, pp. 241–253. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-15014-2_20

    Chapter  Google Scholar 

  40. Weber, G., Bremer, P.T., Pascucci, V.: Topological landscapes: a terrain metaphor for scientific data. IEEE Trans. Vis. Comput. Graph. 13(6), 1416–1423 (2007). https://doi.org/10.1109/TVCG.2007.70601

    Article  Google Scholar 

  41. Weber, G.H., Bremer, P.T., Pascucci, V.: Topological cacti: visualizing contour-based statistics. In: Peikert, R., Hauser, H., Carr, H., Fuchs, R. (eds.) Topological Methods in Data Analysis and Visualization II, pp. 63–76. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-23175-9_5

    Chapter  Google Scholar 

  42. Wu, K., Zhang, S.: A contour tree based visualization for exploring data with uncertainty. Int. J. Uncertain. Quantif. 3(3), 203–223 (2013). https://doi.org/10.1615/Int.J.UncertaintyQuantification.2012003956

    Article  MathSciNet  Google Scholar 

  43. Zhang, Y., Wang, Y., Parthasarathy, S.: Visualizing attributed graphs via terrain metaphor. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, pp. 1325–1334. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3097983.3098130

Download references

Acknowledgements

This work was supported in part by DFG grants MO 3050/2-1 and LI 1530/21-2.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vladimir Molchanov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Evers, M., Herick, M., Molchanov, V., Linsen, L. (2022). Coherent Topological Landscapes for Simulation Ensembles. In: Bouatouch, K., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2020. Communications in Computer and Information Science, vol 1474. Springer, Cham. https://doi.org/10.1007/978-3-030-94893-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94893-1_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94892-4

  • Online ISBN: 978-3-030-94893-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics