Depth perception in disparity-defined objects: finding the balance between averaging and segregation

Philip Peter Kenneth Cammack, Julie Harris

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
7 Downloads (Pure)

Abstract

Deciding what constitutes an object, and what background, is an essential task for the visual system. This presents a conundrum: averaging over the visual scene is required to obtain a precise signal for object segregation, but segregation is required to define the region over which averaging should take place. Depth obtained via binocular disparity (the differences between two eyes’ views), could help with segregation by enabling identification of object and background via differences in depth. Here, we explore depth perception in disparity-defined objects. We show that a simple object segregation rule, followed by averaging over that segregated area, can account for depth estimation errors. To do this, we compared objects with smoothly varying depth edges to those with sharp depth edges, and found that perceived peak depth was reduced for the former. A computational model used a rule based on object shape to segregate and average over a central portion of the object, and was able to emulate the reduction in perceived depth. We also demonstrated that the segregated area is not predefined but is dependent on the object shape. We discuss how this segregation strategy could be employed by animals seeking to deter binocular predators.
Original languageEnglish
Article number20150258
Number of pages11
JournalPhilosophical Transactions of the Royal Society. B, Biological Sciences
Volume371
Issue number1697
Early online date6 Jun 2016
DOIs
Publication statusPublished - 19 Jun 2016

Keywords

  • Stereopsis
  • Binocular disparity
  • Depth perception
  • Disparity averaging
  • Object segregation
  • Psycophysics

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