Low-Level Image Cues in the Perception of Translucent Materials

Roland W. Fleming, Heinrich H. Bülthoff

Research output: Contribution to journalArticlepeer-review

144 Citations (Scopus)


When light strikes a translucent material (such as wax, milk or fruit flesh), it enters the body of the object, scatters and reemerges from the surface. The diffusion of light through translucent materials gives them a characteristic visual softness and glow. What image properties underlie this distinctive appearance? What cues allow us to tell whether a surface is translucent or opaque? Previous work on the perception of semitransparent materials was based on a very restricted physical model of thin filters [Metelli 1970; 1974a, b]. However, recent advances in computer graphics [Jensen et al. 2001; Jensen and Buhler 2002] allow us to efficiently simulate the complex subsurface light transport effects that occur in real translucent objects. Here we use this model to study the perception of translucency, using a combination of psychophysics and image statistics. We find that many of the cues that were traditionally thought to be important for semitransparent filters (e.g., X-junctions) are not relevant for solid translucent objects. We discuss the role of highlights, color, object size, contrast, blur, and lighting direction in the perception of translucency. We argue that the physics of translucency are too complex for the visual system to estimate intrinsic physical parameters by inverse optics. Instead, we suggest that we identify translucent materials by parsing them into key regions and by gathering image statistics from these regions.

Original languageEnglish
Pages (from-to)346-382
Number of pages37
JournalACM Transactions on Applied Perception
Issue number3
Publication statusPublished - 2005


  • Experimentation
  • Human Factors
  • Human visual perception
  • Metelli
  • illumination
  • image statistics
  • material perception
  • translucency
  • transparency

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)
  • Experimental and Cognitive Psychology


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