Using naming time to evaluate quality predictors for model simplification

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. Keywords Model simplification, simplification metrics, image quality, naming time, human vision.

• Type: Conference
• Year: 2000
• Where: CHI
• Authors: Benjamin Watson, Alinda Friedman, Aaron McGaffey
• Keywords: CHI 2000, Human Computer Interaction, Model Simplification, Model Simplification Researchers, Quality Predictors