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Understanding and simulating border ownership centered segmentation.

Tianlong Chen1, Xuemei Cheng2, Thomas Tsao2

  • 1OpticArray Technologies, Inc. 1917 Dundee Road, Rockville, MD, 20850, USA. tc@tichen.net.

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|December 11, 2025
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Summary
This summary is machine-generated.

This study advances border ownership-centered segmentation by exploring binocular disparity and object representations. It proposes a comprehensive model for figure-ground organization, bridging biological vision and computer vision applications.

Keywords:
Active neuronsBorder ownershipCategory selectivityChannel representationContrast-defined objectsDisparityDisparity representationDisparity-defined objectsDistributednessFigure-ground organizationGestalt principlesGlobal context awarenessIllusory objectsObject borderOwner-side directionSegmentationSurface filling-in

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Area of Science:

  • Neuroscience
  • Computer Vision
  • Computational Neuroscience

Background:

  • Border ownership is crucial for object segmentation and figure-ground organization in biological vision.
  • Prior work introduced a channel-based representation for border ownership and category selectivity.

Purpose of the Study:

  • To address remaining challenges in border ownership-centered segmentation.
  • To develop a comprehensive model for figure-ground organization integrating biological and computational insights.

Main Methods:

  • Exploration of binocular disparity representation and generation of binocular border ownership.
  • Investigation of border ownership for illusory and contour-defined objects, including the Rubin Face-Vase illusion.
  • Proposal of 'object pointers' using hypothetical active neurons for 'surface filling-in' and 'instance masks'.

Main Results:

  • Demonstrated methods for generating binocular border ownership for various object types.
  • Introduced a novel mechanism ('object pointers') to address neural processes and computer vision tasks.
  • Synthesized findings into a comprehensive model for border ownership-centered figure-ground organization.

Conclusions:

  • The proposed model integrates global context, distributed processing, and multi-cue representations.
  • This work bridges the gap between biological vision systems and computational applications.
  • Advances understanding and application of border ownership in vision science.