Volume composition and evaluation using eye-tracking data

This article presents a method for automating rendering parameter selection to simplify tedious user interaction and improve the usability of visualization systems. Our approach acquires the important/interesting regions of a dataset through simple user interaction with an eye tracker. Based on this importance information, we automatically compute reasonable rendering parameters using a set of heuristic rules, which are adapted from visualization experience and psychophysical experiments. A user study has been conducted to evaluate these rendering parameters, and while the parameter selections for a specific visualization result are subjective, our approach provides good preliminary results for general users while allowing additional control adjustment. Furthermore, our system improves the interactivity of a visualization system by significantly reducing the required amount of parameter selections and providing good initial rendering parameters for newly acquired datasets of similar types.

[1]  M. Livio.  The Golden Ratio: The Story of Phi, the World's Most Astonishing Number , 2002 .

[2]  Ali Shokoufandeh,et al.  Approximation of canonical sets and their applications to 2D view simplification , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[3]  Päivi Majaranta,et al.  Twenty years of eye typing: systems and design issues , 2002, ETRA.

[4]  Han-Wei Shen,et al.  Dynamic View Selection for Time-Varying Volumes , 2006, IEEE Transactions on Visualization and Computer Graphics.

[5]  Ravin Balakrishnan,et al.  Using deformations for browsing volumetric data , 2003, IEEE Visualization, 2003. VIS 2003..

[6]  Erik Reinhard,et al.  Face-based luminance matching for perceptual colormap generation , 2002, IEEE Visualization, 2002. VIS 2002..

[7]  Guang-Zhong Yang,et al.  Visual feature extraction via eye tracking for saliency driven 2D/3D registration , 2004, ETRA.

[8]  Jock D. Mackinlay,et al.  Automating the design of graphical presentations of relational information , 1986, TOGS.

[9]  Steven K. Feiner,et al.  AutoVisual: rule-based design of interactive multivariate visualizations , 1993, IEEE Computer Graphics and Applications.

[10]  Pat Hanrahan,et al.  Designing effective step-by-step assembly instructions , 2003, ACM Trans. Graph..

[11]  Penny Rheingans,et al.  Perceptual Principles for Effective Visualizations , 1995, Perceptual Issues in Visualization.

[12]  David S. Ebert,et al.  Illustrative Interactive Stipple Rendering , 2003, IEEE Trans. Vis. Comput. Graph..

[13]  R. Krull,et al.  Canonical views in procedural graphics , 2003, IEEE International Professional Communication Conference, 2003. IPCC 2003. Proceedings..

[14]  Erkki Sutinen,et al.  Program Visualization: Comparing Eye-Tracking Patterns with Comprehension Summaries , 2006, PPIG.

[15]  John Dingliana,et al.  Collisions and perception , 2001, TOGS.

[16]  Erik Reinhard,et al.  Artistic Composition for Image Creation , 2001, Rendering Techniques.

[17]  Hideyuki Tamura,et al.  Gaze-directed adaptive rendering for interacting with virtual space , 1996, Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium.

[18]  John F. Hughes,et al.  User-guided composition effects for art-based rendering , 2001, I3D '01.

[19]  G. Kindlmann,et al.  Semi-automatic generation of transfer functions for direct volume rendering , 1998, IEEE Symposium on Volume Visualization (Cat. No.989EX300).

[20]  Victoria Interrante,et al.  Conveying shape with texture: experimental investigations of texture's effects on shape categorization judgments , 2004, IEEE Transactions on Visualization and Computer Graphics.

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

[22]  Thomas Strothotte,et al.  Capturing and Re‐Using Rendition Styles for Non‐Photorealistic Rendering , 1999, Comput. Graph. Forum.

[23]  Douglas DeCarlo,et al.  Stylization and abstraction of photographs , 2002, ACM Trans. Graph..

[24]  Mahdi Nezamabadi,et al.  Color Appearance Models , 2014, J. Electronic Imaging.

[25]  Gordon L. Kindlmann,et al.  Semi-Automatic Generation of Transfer Functions for Direct Volume Rendering , 1998, VVS.

[26]  A. Finkelstein,et al.  Nonphotorealistic rendering , 2003, IEEE Computer Graphics and Applications.

[27]  Steven K. Feiner,et al.  Automated generation of intent-based 3D Illustrations , 1991, SIGGRAPH.

[28]  Bernice E. Rogowitz,et al.  A rule-based tool for assisting colormap selection , 1995, Proceedings Visualization '95.

[29]  Martin Reddy,et al.  Perceptually Optimized 3D Graphics , 2001, IEEE Computer Graphics and Applications.

[30]  Thomas Ertl,et al.  Interactive Cutaway Illustrations , 2003, Comput. Graph. Forum.

[31]  David Salesin,et al.  The virtual cinematographer: a paradigm for automatic real-time camera control and directing , 1996, SIGGRAPH.

[32]  Keith Rayner.  Eye movements as reflections of perceptual and cognitive processes (abstract only) , 2004, ETRA.

[33]  Wenzhong Shi,et al.  Topology for 3D spatial objects , 2003 .

[34]  Christopher G. Healey,et al.  Choosing effective colours for data visualization , 1996, Proceedings of Seventh Annual IEEE Visualization '96.

[35]  Richard A. Bolt,et al.  A gaze-responsive self-disclosing display , 1990, CHI '90.

[36]  Douglas DeCarlo,et al.  Visual interest and NPR: an evaluation and manifesto , 2004, NPAR '04.

[37]  Robert J. K. Jacob,et al.  Interacting with eye movements in virtual environments , 2000, CHI.

[38]  Marc Levoy,et al.  Gaze-directed volume rendering , 1990, I3D '90.

[39]  Adam Finkelstein,et al.  Directing gaze in 3D models with stylized focus , 2006, EGSR '06.

[40]  Jason Jerald,et al.  Eye gaze correction for videoconferencing , 2002, ETRA.

[41]  M. Lévesque.  Perception , 1986, The Yale Journal of Biology and Medicine.

[42]  B. Laeng,et al.  Canonical views of faces and the cerebral hemispheres , 2001, Laterality.

[43]  Michael F. Cohen,et al.  Radioptimization: goal based rendering , 1993, SIGGRAPH.

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

[45]  Stefan Schlechtweg,et al.  Non-photorealistic computer graphics: modeling, rendering, and animation , 2002 .

[46]  Penny Rheingans,et al.  A tool for dynamic explorations of color mappings , 1990, I3D '90.

[47]  D. Puro.  The Retina. An Approachable Part of the Brain , 1988 .

[48]  David S. Ebert,et al.  Illustration motifs for effective medical volume illustration , 2005, IEEE Computer Graphics and Applications.

[49]  John Dingliana,et al.  Eye movements and interactive graphics , 2003 .

[50]  Knut Hartmann,et al.  Dynamic Visual Emphasis in Interactive Technical Documentation , 1998 .

[51]  Matthias Zwicker,et al.  EWA volume splatting , 2001, Proceedings Visualization, 2001. VIS '01..

[52]  Mateu Sbert,et al.  Viewpoint Selection using Viewpoint Entropy , 2001, VMV.

[53]  M J Tarr,et al.  What Object Attributes Determine Canonical Views? , 1999, Perception.

[54]  Ivan Viola,et al.  Importance-driven volume rendering , 2004, IEEE Visualization 2004.

[55]  Thomas Rist,et al.  AWI: a workbench for semi-automated illustration design , 1994, AVI '94.

[56]  M. Stella Atkins,et al.  Eye gaze patterns differentiate novice and experts in a virtual laparoscopic surgery training environment , 2004, ETRA.

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

[58]  David R. Forsey,et al.  How to Render Frames and Influence People , 1994, Comput. Graph. Forum.

[59]  Han-Wei Shen,et al.  View selection for volume rendering , 2005, VIS 05. IEEE Visualization, 2005..

[60]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[61]  Yuriko Takeshima,et al.  A feature-driven approach to locating optimal viewpoints for volume visualization , 2005, VIS 05. IEEE Visualization, 2005..

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