Interactive Storyboard for Overall Time-Varying Data Visualization

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 through data analysis processes. Our storyboard system integrates automatic visualization generation methods and interactive adjustment procedures to provide new tools for visualizing and exploring time-varying datasets. We also provide a flexible framework to quantify data differences and automatically select representative datasets through exploring scientific data distribution features. Since this approach reduces the visualized data amount into a more understandable size and format for users, it can be used to effectively visualize, represent, and explore a large time-varying dataset. Initial user study results show that our approach shortens the exploration time and reduces the number of datasets that users visualized individually. This visualization method is especially useful for situations that require close observance or are not capable of interactive rendering, such as documentation and demonstration.

[1]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[2]  V. Mcgee Multidimensional Scaling Of N Sets Of Similarity Measures: A Nonmetric Individual Differences Approach. , 1968, Multivariate behavioral research.

[3]  William H. Press,et al.  Book-Review - Numerical Recipes in Pascal - the Art of Scientific Computing , 1989 .

[4]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[5]  Edward H. Adelson,et al.  Motion without movement , 1991, SIGGRAPH.

[6]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[7]  Deborah Silver,et al.  Visualizing features and tracking their evolution , 1994, Computer.

[8]  David C. Banks,et al.  A Predictor-Corrector Technique for Visualizing Unsteady Flow , 1995, IEEE Trans. Vis. Comput. Graph..

[9]  B. Wandell Foundations of vision , 1995 .

[10]  F. Famoye Seeing Through Statistics , 1995 .

[11]  Sheila O'Leary Weaver,et al.  Seeing through statistics , 1996 .

[12]  Paul A. Beardsley,et al.  Design galleries: a general approach to setting parameters for computer graphics and animation , 1997, SIGGRAPH.

[13]  Xin Wang,et al.  Tracking and Visualizing Turbulent 3D Features , 1997, IEEE Trans. Vis. Comput. Graph..

[14]  David S. Doermann,et al.  Video summarization by curve simplification , 1998, MULTIMEDIA '98.

[15]  P. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 1999 .

[16]  Kwan-Liu Ma,et al.  Image graphs-a novel approach to visual data exploration , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[17]  Renato Pajarola,et al.  Topology preserving and controlled topology simplifying multiresolution isosurface extraction , 2000 .

[18]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[19]  Hans J. W. Spoelder,et al.  Visualization of time-dependent data with feature tracking and event detection , 2001, The Visual Computer.

[20]  Jessica K. Hodgins,et al.  Interactive control of avatars animated with human motion data , 2002, SIGGRAPH.

[21]  Patrick Pérez,et al.  Rapid Summarisation and Browsing of Video Sequences , 2002, BMVC.

[22]  Chaoli Wang,et al.  High dimensional direct rendering of time-varying volumetric data , 2003, IEEE Visualization, 2003. VIS 2003..

[23]  Han-Wei Shen,et al.  Volume tracking using higher dimensional isosurfacing , 2003, IEEE Visualization, 2003. VIS 2003..

[24]  Ilan Shimshoni,et al.  Mean shift based clustering in high dimensions: a texture classification example , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[25]  Stefan Carlsson,et al.  Pose-based clustering in action sequences , 2003, First IEEE International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis, 2003. HLK 2003..

[26]  Vivek Verma,et al.  Comparative flow visualization , 2004, IEEE Transactions on Visualization and Computer Graphics.

[27]  Valerio Pascucci,et al.  Time-varying reeb graphs for continuous space-time data , 2004, SCG '04.

[28]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[29]  Markus H. Gross,et al.  Scalable 3D video of dynamic scenes , 2005, The Visual Computer.

[30]  Daniel Cohen-Or,et al.  Action synopsis: pose selection and illustration , 2005, ACM Trans. Graph..

[31]  Yuriko Takeshima,et al.  T-Map: A Topological Approach to Visual Exploration of Time-Varying Volume Data , 2005, ISHPC.

[32]  Charles Hansen,et al.  The Visualization Handbook , 2011 .

[33]  Penny Rheingans,et al.  Illustration-inspired techniques for visualizing time-varying data , 2005, VIS 05. IEEE Visualization, 2005..

[34]  David Salesin,et al.  Schematic storyboarding for video visualization and editing , 2006, SIGGRAPH 2006.

[35]  Min Chen,et al.  Visual Signatures in Video Visualization , 2006, IEEE Transactions on Visualization and Computer Graphics.

[36]  Mateu Sbert,et al.  Importance-Driven Focus of Attention , 2006, IEEE Transactions on Visualization and Computer Graphics.

[37]  Chandrajit L. Bajaj,et al.  Time-varying contour topology , 2006, IEEE Transactions on Visualization and Computer Graphics.

[38]  Janko Calic,et al.  Compact Visualisation of Video Summaries , 2007, EURASIP J. Adv. Signal Process..

[39]  Helwig Hauser,et al.  Story Telling for Presentation in Volume Visualization , 2007, EuroVis.

[40]  Hiroshi Akibay,et al.  A tri-space visualization interface for analyzing time-varying multivariate volume data , 2007 .

[41]  Jehee Lee Interactive Control of Avatars Animated with Human Motion Data , .