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Large amounts of time-varying datasets create great challenges for users to understand and explore them. This paper proposes an efficient visualization method for observing overall data contents and changes throughout an entire time-varying dataset. We develop an interactive storyboard approach by composing sample volume renderings and descriptive geometric primitives that are generated ...



















While the treemap is a popular method for visualizing hierarchical data, it is often difficult for the users to track layout and attribute changes when the data evolve over time. When viewing the Treemaps side by side or back and forth, there exist several problems that can prevent the viewers from performing effective comparisons. Those problems include abrupt layout changes, a lack of prominent visual patterns to ...
















Animation is an effective way to show how time-varying phenomena evolve over time. A key issue of generating a good animation is to select ideal views through which the user can perceive the maximum amount of information from the time-varying dataset. In this paper, we rst propose an improved view selection method for static data. The method measures the quality of a static view by analyzing ...
















Many volume data possess symmetric features that can be clearly observed, for example, those existing in diffusion tensor image data sets. The exploitations of symmetries for volume data sets, however, are relatively limited due to the prohibitive computational cost of detecting the symmetries. In this paper we present an efficient parallel algorithm for symmetry computation in volume data represented ...








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The Ohio State University, Department of Computer Science and Engineering

395 Dreese Lab, 2015 Neil Avenue, Columbus OH 43210

Professor Han-Wei Shen

hwshen@cse.ohio-state.edu (V) 614 292 0060  (F) 614 2922911