Volume composition and evaluation using eye-tracking data

Aidong Lu, Ross Maciejewski, David S. Ebert

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Article number4
JournalACM Transactions on Applied Perception
Issue number1
StatePublished - Jan 2010
Externally publishedYes


  • Eye tracker
  • Illustrative visualization
  • Interaction
  • Usability and human factors in visualization
  • Volume rendering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science
  • Experimental and Cognitive Psychology


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