Issue No. 03 - March (2013 vol. 19)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2012.143
Jun Tao , Dept. of Comput. Sci., Michigan Technol. Univ., Houghton, MI, USA
Jun Ma , Dept. of Comput. Sci., Michigan Technol. Univ., Houghton, MI, USA
Chaoli Wang , Dept. of Comput. Sci., Michigan Technol. Univ., Houghton, MI, USA
Ching-Kuang Shene , Dept. of Comput. Sci., Michigan Technol. Univ., Houghton, MI, USA
We treat streamline selection and viewpoint selection as symmetric problems which are formulated into a unified information-theoretic framework. This is achieved by building two interrelated information channels between a pool of candidate streamlines and a set of sample viewpoints. We define the streamline information to select best streamlines and in a similar manner, define the viewpoint information to select best viewpoints. Furthermore, we propose solutions to streamline clustering and viewpoint partitioning based on the representativeness of streamlines and viewpoints, respectively. Finally, we define a camera path that passes through all selected viewpoints for automatic flow field exploration. We demonstrate the robustness of our approach by showing experimental results with different flow data sets, and conducting rigorous comparisons between our algorithm and other seed placement or streamline selection algorithms based on information theory.
Vectors, Shape, Mutual information, Silicon, Probability distribution, Cameras, Data visualization
Jun Tao, Jun Ma, Chaoli Wang and Ching-Kuang Shene, "A Unified Approach to Streamline Selection and Viewpoint Selection for 3D Flow Visualization," in IEEE Transactions on Visualization & Computer Graphics, vol. 19, no. 3, pp. 393-406, 2013.