EGPGV17: Eurographics Symposium on Parallel Graphics and Visualization

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2631432

Browse

Browsing EGPGV17: Eurographics Symposium on Parallel Graphics and Visualization by Subject "Computing methodologies"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Interactive Exploration of Dissipation Element Geometry
    (The Eurographics Association, 2017) Vierjahn, Tom; Schnorr, Andrea; Weyers, Benjamin; Denker, Dominik; Wald, Ingo; Garth, Christoph; Kuhlen, Torsten W.; Hentschel, Bernd; Alexandru Telea and Janine Bennett
    Dissipation elements (DE) define a geometrical structure for the analysis of small-scale turbulence. Existing analyses based on DEs focus on a statistical treatment of large populations of DEs. In this paper, we propose a method for the interactive visualization of the geometrical shape of DE populations. We follow a two-step approach: in a pre-processing step, we approximate individual DEs by tube-like, implicit shapes with elliptical cross sections of varying radii; we then render these approximations by direct ray-casting thereby avoiding the need for costly generation of detailed, explicit geometry for rasterization. Our results demonstrate that the approximation gives a reasonable representation of DE geometries and the rendering performance is suitable for interactive use.
  • Thumbnail Image
    Item
    Photo-Guided Exploration of Volume Data Features
    (The Eurographics Association, 2017) Raji, Mohammad; Hota, Alok; Sisneros, Robert; Messmer, Peter; Huang, Jian; Alexandru Telea and Janine Bennett
    In this work, we pose the question of whether, by considering qualitative information such as a sample target image as input, one can produce a rendered image of scientific data that is similar to the target. The algorithm resulting from our research allows one to ask the question of whether features like those in the target image exists in a given dataset. In that way, our method is one of imagery query or reverse engineering, as opposed to manual parameter tweaking of the full visualization pipeline. For target images, we can use real-world photographs of physical phenomena. Our method leverages deep neural networks and evolutionary optimization. Using a trained similarity function that measures the difference between renderings of a phenomenon and real-world photographs, our method optimizes rendering parameters. We demonstrate the efficacy of our method using a superstorm simulation dataset and images found online. We also discuss a parallel implementation of our method, which was run on NCSA's Blue Waters.