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Interactive Visual Clutter Management in Scientific Visualization

Tong, Xin
http://rave.ohiolink.edu/etdc/view?acc_num=osu1471612150

Abstract Details

2016, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
Scientists visualize their data and interact with them on computers in order to thoroughly understand them. Nowadays, data become so large and complex that it is impossible to display the entire data on a single image. Scientific visualization often suffers from visual clutter problem because of high spacial resolution/dimension and temporal resolution. Interacting with the visualizations of large data, on the other hand, allows users to dynamically explore different parts of the data and gradually understand all information in the data. Information congestion and visual clutter exist in visualizations of different kinds of data, such as flow field data, tensor field data, and time-varying data. Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. Glyph as a powerful multivariate visualization technique is used to visualize data through its visual channels. Placing large number of glyphs over the entire 3D space results in occlusion and visual clutter that make the visualization ineffective. To avoid the occlusion in streamline and glyph visualization, we propose a view-dependent interactive 3D lens that removes the occluding streamlines/glyphs by pulling the them aside through animations. High resolution simulations are capable of generating very large vector fields that are expensive to store and analyze. In addition, the noise and/or uncertainty contained in the data often affects the quality of visualization by producing visual clutter that interferes with both the interpretation and identification of important features. Instead, we can store the distributions of many vector orientations and visualize the distributions with 3D glyphs, which largely reduce visual clutter. Empowered by rapid advance of high performance computer architectures and software, it is now possible for scientists to perform high temporal resolution simulations with unprecedented accuracy. The large number of time steps makes it difficult to perform post analysis and visualization after the computation is completed. Instead of visualize all the time steps, users filter the original data that is too large to be all visualized and interactively pick the interesting parts of the data to display. To achieve this goal, we provide a time-varying data exploration system that allows users to pick the most salient time steps with Dynamic Time Warping (DTW) algorithm and then only visualize the data volumes corresponding to those time steps. We generalize three general strategies to manage visual clutter and demonstrate them using four visualization techniques. In the end of this dissertation, we present possible directions of future works that may inspire the readers to do more researches on visual clutter management for different data and applications.
Han-Wei Shen (Advisor)
Huamin Wang (Committee Member)
Arnab Nandi (Committee Member)
179 p.
Computer Engineering; Computer Science
visual clutter; view-dependent; focus-context; glyph-based visualization; virtual reality; touch screen; flow visualization; streamline; white matter tracts; deformation; occlusion management; time-varying data; vector field distribution; key time step

Recommended Citations

Citations

  • Tong, X. (2016). Interactive Visual Clutter Management in Scientific Visualization [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471612150

    APA Style (7th edition)

  • Tong, Xin. Interactive Visual Clutter Management in Scientific Visualization. 2016. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1471612150.

    MLA Style (8th edition)

  • Tong, Xin. "Interactive Visual Clutter Management in Scientific Visualization." Doctoral dissertation, Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471612150

    Chicago Manual of Style (17th edition)

osu1471612150
1,083
© 2016, some rights reserved.Creative Commons License Interactive Visual Clutter Management in Scientific Visualization by Xin Tong is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.