Volume composition and evaluation using eye-tracking data

  • Authors:
  • Aidong Lu;Ross Maciejewski;David S. Ebert

  • Affiliations:
  • University of North Carolina at Charlotte;Purdue University;Purdue University

  • Venue:
  • ACM Transactions on Applied Perception (TAP)
  • Year:
  • 2010

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Abstract

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.