Volume Composition and Evaluation Using Eye-Tracking Data
Aidong Lu, Ross Maciejewski, David S. Ebert
In ACM Transactions on Applied Perception, 7(1), January 2010.
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.
Keyword(s): Usability, human factors in visualization, eye tracker, illustrative visualization, interaction, volume rendering
Article URL: http://doi.acm.org/10.1145/1658349.1658353
BibTeX format:
@article{Lu:2010:VCA,
  author = {Aidong Lu and Ross Maciejewski and David S. Ebert},
  title = {Volume Composition and Evaluation Using Eye-Tracking Data},
  journal = {ACM Transactions on Applied Perception},
  volume = {7},
  number = {1},
  pages = {4:1--4:20},
  month = jan,
  year = {2010},
}
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