FlowTour: An Automatic Guide for Exploring Internal Flow Features

Jun Ma, James Walker, Chaoli Wang, Scott A. Kuhl, Ching-Kuang Shene
Michigan Technological University
IEEE Pacific Visualization Symposium, 2014


   title={FlowString: Partial Streamline Matching Using Shape Invariant Similarity Measure for Exploratory Flow Visualization},

   author={Tao, Jun and Wang, Chaoli and Shene, Ching-Kuang},



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We present FlowTour, a novel framework that provides an automatic guide for exploring internal flow features. Our algorithm first identifies critical regions and extracts their skeletons for feature characterization and streamline placement. We then create candidate viewpoints based on the construction of a simplified mesh enclosing each critical region and select best viewpoints based on a viewpoint quality measure. Finally, we design a tour that traverses all selected viewpoints in a smooth and efficient manner for visual navigation and exploration of the flow field. Unlike most existing works which only consider external viewpoints, a unique contribution of our work is that we also incorporate internal viewpoints to enable a clear observation of what lies inside of the flow field. Our algorithm is thus particularly useful for exploring hidden or occluded flow features in a large and complex flow field. We demonstrate our algorithm with several flow data sets and perform a user study to confirm the effectiveness of our approach.
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