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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

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Published on: December 15, 2023

Depth and luminance edges attract.

Alan E Robinson1, Donald I A MacLeod

  • 1Department of Psychology, University of California, San Diego, La Jolla, CA, USA. robinson@cogsci.ucsd.edu

Journal of Vision
|September 10, 2013
PubMed
Summary
This summary is machine-generated.

Visual perception combines depth and luminance cues for edge location. Luminance cues often dominate, but both contribute adaptively, explaining why we don't notice blurry depth edges.

Keywords:
cue combinationedge detectionsensor fusion

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Area of Science:

  • Vision science
  • Perceptual psychology
  • Computational neuroscience

Background:

  • Spatial resolution of disparity perception is lower than luminance perception.
  • Depth-defined edges appear less sharp than luminance-defined edges, yet this difference is not readily perceived.
  • The visual system's integration of multiple sensory cues is crucial for robust perception.

Purpose of the Study:

  • To investigate how the visual system combines depth and luminance information for edge localization.
  • To determine if a cue-combination model can explain the perceived location of edges defined by different visual cues.
  • To understand the weighting mechanisms applied to depth and luminance cues in perceptual tasks.

Main Methods:

  • Participants judged the perceived location of depth-defined and luminance-defined edges.
  • Stimuli involved edges separated by varying spatial acuities, up to 5.6 min of arc.
  • A computational model of optimal cue combination was used for data analysis.

Main Results:

  • Perceived edge location was influenced by both depth-defined and luminance-defined edges.
  • Luminance-defined edges tended to have a stronger influence on the final percept.
  • Data supported an optimal cue-combination model, though not fully explained, suggesting adaptive weighting.

Conclusions:

  • Both depth and luminance cues are integrated in visual perception for edge localization.
  • The visual system adaptively weights these cues based on task demands and individual cue reliability.
  • This adaptive weighting mechanism likely explains the lack of noticeable blurriness in depth-defined edges.