Structure-accentuating dense flow visualization

  • Authors:

  • Sung W. Park;Hongfeng Yu;Ingrid Hotz;Oliver Kreylos;Lars Linsen;Bernd Hamann

  • Affiliations:

  • Institute for Data Analysis and Visualization, University of California, Davis, Davis, CA;Institute for Data Analysis and Visualization, University of California, Davis, Davis, CA;Institute for Data Analysis and Visualization, University of California, Davis, Davis, CA;Institute for Data Analysis and Visualization, University of California, Davis, Davis, CA;Department of Mathematics and Computer Science, Ernst-Moritz-Arndt-Universität Greifswald, Greifswald, Germany;Institute for Data Analysis and Visualization, University of California, Davis, Davis, CA

  • Venue:
  • EUROVIS'06 Proceedings of the Eighth Joint Eurographics / IEEE VGTC conference on Visualization
  • Year:
  • 2006

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Abstract

Vector field visualization approaches can broadly be categorized into approaches that directly visualize local or integrated flow and approaches that analyze the topological structure and visualize extracted features. Our goal was to come up with a method that falls into the first category, yet reveals structural information. We have developed a dense flow visualization method that shows the overall flow behavior while accentuating structural information without performing a topological analysis. Our method is based on a geometry-based flow integration step and a texture-based visual exploration step. The flow integration step generates a density field, which is written into a texture. The density field is generated by tracing particles under the influence of the underlying vector field. When using a quasi-random seeding strategy for initialization, the resulting density is high in attracting regions and low in repelling regions. Density is measured by the number of particles per region accumulated over time. We generate one density field using forward and one using backward propagation. The density fields are explored using texture-based rendering techniques. We generate the two output images separately and blend the results, which allows us to distinguish between inflow and outflow regions. We obtained dense flow visualizations that display the overall flow behavior, emphasize critical and separating regions, and indicate flow direction in the neighborhood of these regions. We have test our method for isolated first-order singularities and real data sets.