A04 | Quantitative Models for Visual Abstraction

Prof. Oliver Deussen, Universität Konstanz
Email | Website

Oliver Deussen

Prof. Heinrich H. Bülthoff, Max Planck Institute for Biological Cybernetics
Email | Website

Heinrich H. Bülthoff

Marc Spicker, Universität Konstanz – Email | Website

We aim at finding abstraction methods for visual computing that create graphical representations for given data with a quantitatively determined degree of abstraction. Appropriate abstraction styles will be selected and representations will be developed that allow us to technically quantify the visual representation (e.g. the number of graphical elements used for a representation). In a second step, we will develop methods that perform abstraction also in a perceptually linear way.

This project will provide visual abstraction methods for other projects of the Collaborative Research Center.

Research questions

Can we measure the degree of abstraction of a non-photorealistic rendering?

Can we create abstraction methods with coherence between created abstract representations?

Is it possible to parametrize different abstraction styles in a technically linear way?

Is the parametrization also adjustable in a perceptually linear way?

Representation of an input image (upper left) by illustrations with varying number of graphical elements (24.000, 12.000 and 6.000 stipple points.

Publications

  1. J. Kratt, F. Eisenkeil, M. Spicker, Y. Wang, D. Weiskopf, and O. Deussen, “Structure-aware Stylization of Mountainous Terrains,” in Vision, Modeling & Visualization, 2017.
  2. M. Spicker, F. Hahn, T. Lindemeier, D. Saupe, and O. Deussen, “Quantifying Visual Abstraction Quality for Stipple Drawings,” in Proceedings of NPAR’17, 2017.
  3. P. Tutzauer, S. Becker, T. Niese, O. Deussen, and D. Fritsch, “Understanding Human Perception of Building Categories in Virtual 3d Cities - a User Study,” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLI–B2, pp. 683–687, 2016.
  4. M. Spicker, J. Kratt, D. Arellano, and O. Deussen, Depth-Aware Coherent Line Drawings. ACM, 2015.

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