A01 | Uncertainty Quantification and Analysis in Visual Computing

Prof. Daniel Weiskopf, University of Stuttgart
Email | Website

Prof. Oliver Deussen, University of Konstanz
Email | Website

Daniel Weiskopf
Oliver Deussen

Prof. Andrea Barth, University of Stuttgart
Email | Website

Prof. Miriam Butt, University of Konstanz
Email | Website

Andrea Barth
Miriam Butt

Christoph Schulz, University of Stuttgart – Email | Website

Jochen Görtler, University of Konstanz – Email | Website

Our long-term goal is the modeling, handling, and quantification of uncertainty throughout the complete visual computing process, from visual-computing specific models of uncertainty to the visual representation of, and interaction with, uncertainty information. We aim to investigate models and methods for quantifying individual sources of uncertainty, the propagation of uncertainty through the visual computing pipeline, and the impact of uncertainty on the components of the visual computing process. Furthermore, we will advance the visualization and visual analytics of uncertainty, and investigate several different application examples.

Research Questions

How can we integrate and combine the different aspects of uncertainty in the various subareas of visual computing?

Can we model the flow and transformation of uncertainty through the visual computing pipeline?

Can we incorporate uncertainty in the visual computing stages and represent their effects on those stages?

Can we support visual analytics of uncertainty?

Fig. 1: Texture-based technique to visualize uncertainty of time dependend particle positions along streamlines.

Fig. 2: Flow Radar Glyphs.

Publications

  1. J. Görtler, C. Schulz, O. Deussen, and D. Weiskopf, “Bubble Treemaps for Uncertainty Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 719–728, 2018.
  2. C. Schulz, M. Burch, and D. Weiskopf, “Visual Data Cleansing of Eye Tracking Data,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), 2015.
  3. K. Kurzhals, M. Hlawatsch, M. Burch, and D. Weiskopf, “Fixation-Image Charts,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), 2016, vol. 1, pp. 11–18.
  4. J. Görtler, M. Spicker, C. Schulz, D. Weiskopf, and O. Deussen, “Stippling of 2D Scalar Fields,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 6, pp. 2193–2204, 2019.
  5. C. Schulz et al., “Generative Data Models for Validation and Evaluation of Visualization Techniques,” in Proceedings of the Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV), 2016, pp. 112–124.
  6. D. Weiskopf, M. Burch, L. L. Chuang, B. Fischer, and A. Schmidt, Eye Tracking and Visualization: Foundations, Techniques, and Applications. Berlin, Heidelberg: Springer, 2016.
  7. T. Blascheck, F. Beck, S. Baltes, T. Ertl, and D. Weiskopf, “Visual Analysis and Coding of Data-Rich User Behavior,” in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), 2016, pp. 141–150.
  8. J. Görtler, R. Kehlbeck, and O. Deussen, “A Visual Exploration of Gaussian Processes,” in Proceedings of the Workshop on Visualization for AI Explainability (VISxAI), 2018.
  9. Y. Wang, Z. Wang, C.-W. Fu, H. Schmauder, O. Deussen, and D. Weiskopf, “Image-Based Aspect Ratio Selection.,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 840–849, 2019.
  10. C. Schulz, A. Nocaj, J. Goertler, O. Deussen, U. Brandes, and D. Weiskopf, “Probabilistic Graph Layout for Uncertain Network Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 531–540, 2017.
  11. K. Srulijes et al., “Visualization of Eye-Head Coordination While Walking in Healthy Subjects and Patients with Neurodegenerative Diseases,” Poster (reviewed) presented on Symposium of the International Society of Posture and Gait Research (ISPGR), 2017.
  12. C. Schulz, K. Schatz, M. Krone, M. Braun, T. Ertl, and D. Weiskopf, “Uncertainty Visualization for Secondary Structures of Proteins,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), 2018, pp. 96–105.
  13. T. Spinner, J. Körner, J. Görtler, and O. Deussen, “Towards an Interpretable Latent Space: An Intuitive Comparison of Autoencoders with Variational Autoencoders,” in Proceedings of the Workshop on Visualization for AI Explainability (VISxAI), 2018.
  14. P. Gralka, C. Schulz, G. Reina, D. Weiskopf, and T. Ertl, “Visual Exploration of Memory Traces and Call Stacks,” in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), 2017, pp. 54–63.
  15. K. Kurzhals, B. Fisher, M. Burch, and D. Weiskopf, “Eye Tracking Evaluation of Visual Analytics,” Information Visualization, vol. 15, no. 4, pp. 340–358, 2016.
  16. C. Schulz, A. Zeyfang, M. van Garderen, H. Ben Lahmar, M. Herschel, and D. Weiskopf, “Simultaneous Visual Analysis of Multiple Software Hierarchies,” in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), 2018, pp. 87–95.
  17. C. Schulz, M. Burch, F. Beck, and D. Weiskopf, “Visual Data Cleansing of Low-Level Eye Tracking Data,” in Eye Tracking and Visualization: Foundations, Techniques, and Applications. ETVIS 2015, M. Burch, L. Chuang, B. Fisher, A. Schmidt, and D. Weiskopf, Eds. Springer International Publishing, 2017, pp. 199–216.
  18. C. Schulz, N. Rodrigues, K. Damarla, A. Henicke, and D. Weiskopf, “Visual Exploration of Mainframe Workloads,” in Proceedings of the SIGGRAPH Asia Symposium on Visualization, Article No. 4, 2017.