Using naming time to evaluate quality predictors for model simplification

  • Authors:
  • Benjamin Watson;Alinda Friedman;Aaron McGaffey

  • Affiliations:
  • Dept. Computing Science, 615 General Services Bldg., University of Alberta, Edmonton, Alberta, Canada T6G 2H1;Dept. Psychology, University of Alberta, Edmonton, Alberta, Canada T6G 2E9;Dept. Psychology, University of Alberta, Edmonton, Alberta, Canada T6G 2E9

  • Venue:
  • Proceedings of the SIGCHI conference on Human Factors in Computing Systems
  • Year:
  • 2000

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Abstract

Model simplification researchers require quality heuristics to guide simplification, and quality predictors to allow comparison of different simplification algorithms. However, there has been little evaluation of these heuristics or predictors. We present an evaluation of quality predictors. Our standard of comparison is naming time, a well established measure of recognition from cognitive psychology. Thirty participants named models of familiar objects at three levels of simplification. Results confirm that naming time is sensitive to model simplification. Correlations indicate that view-dependent image quality predictors are most effective for drastic simplifications, while view-independent three-dimensional predictors are better for more moderate simplifications.