Object classification for human and ideal observers

Zili Liu, David C. Knill, Daniel Kersten

Research output: Contribution to journalArticlepeer-review

108 Scopus citations

Abstract

We describe a novel approach, based on ideal observer analysis, for measuring the ability of human observers to use image information for 3D object perception. We compute the statistical efficiency of subjects relative to an ideal observer for a 3D object classification task. After training to 11 different views of a randomly shaped thick wire object, subjects were asked which of a pair of noisy views of the object best matched the learned object. Efficiency relative to the actual information in the stimuli can be as high as 20%. Increases in object regularity (e.g. symmetry) lead to increases in the efficiency with which novel views of an object could be classified. Furthermore, such increases in regularity also lead to decreases in the effect of viewpoint on classification efficiency. Human statistical efficiencies relative to a 2D ideal observer exceeded 100%, thereby excluding all models which are sub-optimal relative to the 2D ideal.

Original languageEnglish (US)
Pages (from-to)549-568
Number of pages20
JournalVision Research
Volume35
Issue number4
DOIs
StatePublished - Feb 1995

Keywords

  • Ideal observers
  • Object recognition
  • Radial basis functions
  • Template matching

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