Using naming time to evaluate quality predictors for model simplification

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.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.6 SIMULATION AND MODELING

Additional Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: Modeling and recovery of physical attributes

General Terms:
Design, Experimentation, Human Factors, Management, Measurement, Performance, Theory

Keywords:
human vision, image quality, model simplification, naming time, simplification metrics