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Approaching human visual perception through AI-based representation of figure-ground segregation.

Chakkai Yip1, Ezekiel Moroze1, Shigeaki Nishina2

  • 1Computational Neuroscience and Vision Laboratory, Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States.

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|March 16, 2026
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Summary
This summary is machine-generated.

Convolutional neural networks (CNNs) can infer border-ownership (BOS) for figure-ground perception from visual context. Geometric configurations, not just edges, are crucial for this process, even with degraded visual input.

Keywords:
AI saliency mappingborder-ownershipcontour junctionsconvolutional neural networksfeedforward processesfigure-ground segregationpartial occlusion

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Area of Science:

  • Computational neuroscience
  • Computer vision
  • Visual perception

Background:

  • Figure-ground perception is essential for object recognition.
  • The computational basis of border-ownership assignment (BOS) remains unclear.
  • Understanding BOS aids in developing artificial visual systems.

Purpose of the Study:

  • To investigate how convolutional neural network (CNN) architectures infer border-ownership (BOS).
  • To determine the role of contextual information in BOS inference under degraded conditions.
  • To explore the hierarchical processing of visual features in CNNs related to BOS.

Main Methods:

  • Trained multiple CNN architectures on overlapping/occlusion stimuli.
  • Tested CNN performance on systematically degraded contours.
  • Analyzed network representations to understand feature hierarchies.

Main Results:

  • CNNs inferred BOS from feedforward computations even with degraded contours.
  • Performance strongly depended on junction-like configurations, highlighting geometric context.
  • Accuracy scaled linearly with contextual information from fragmented borders.
  • Hierarchical progression from local edges to BOS-specific features was observed.

Conclusions:

  • Hierarchical feedforward processing can explain certain aspects of BOS inference.
  • Geometric context is more influential than isolated edges for BOS.
  • Additional mechanisms like feedback may be necessary for robust figure-ground segregation.