Perceptual assignment of opacity to translucent surfaces: The role of image blur

Manish Singh, Barton L. Anderson

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


In constructing the percept of transparency, the visual system must decompose the light intensity at each image location into two components - one for the partially transmissive surface, the other for the underlying surface seen through it. Theories of perceptual transparency have typically assumed that this decomposition is defined quantitatively in terms of the inverse of some physical model (typically, Metelli's 'episcotister model'). In previous work, we demonstrated that the visual system uses Michelson contrast as a critical image variable in assigning transmittance to transparent surfaces - not luminance differences as predicted by Metelli's model [F Metelli, 1974 Scientific American 230(4) 90-98]. In this paper, we study the contribution of another variable in determining perceived transmittance, namely, the image blur introduced by the light-scattering properties of translucent surfaces and materials. Experiment 1 demonstrates that increasing the degree of blur in the region of transparency leads to a lowering in perceived transmittance, even if Michelson contrast remains constant in this region. Experiment 2 tests how this addition of blur affects apparent contrast in the absence of perceived transparency. The results demonstrate that, although introducing blur leads to a lowering in apparent contrast, the magnitude of this decrease is relatively small, and not sufficient to explain the decrease in perceived transmittance observed in experiment 1. The visual system thus takes the presence of blur in the region of transparency as an additional image cue in assigning transmittance to partially transmissive surfaces.

Original languageEnglish (US)
Pages (from-to)531-552
Number of pages22
Issue number5
StatePublished - 2002

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Ophthalmology
  • Sensory Systems
  • Artificial Intelligence

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