Generalization of figure-ground segmentation from binocular to monocular vision in an embodied biological brain model

  • Authors:

  • Brian Mingus;Trent Kriete;Seth Herd;Dean Wyatte;Kenneth Latimer;Randy O'Reilly

  • Affiliations:

  • Computational Cognitive Neuroscience Lab, Department of Psychology, University of Colorado at Boulder, Boulder, Co;Computational Cognitive Neuroscience Lab, Department of Psychology, University of Colorado at Boulder, Boulder, Co;Computational Cognitive Neuroscience Lab, Department of Psychology, University of Colorado at Boulder, Boulder, Co;Computational Cognitive Neuroscience Lab, Department of Psychology, University of Colorado at Boulder, Boulder, Co;Computational Cognitive Neuroscience Lab, Department of Psychology, University of Colorado at Boulder, Boulder, Co;Computational Cognitive Neuroscience Lab, Department of Psychology, University of Colorado at Boulder, Boulder, Co

  • Venue:
  • AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
  • Year:
  • 2011

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Abstract

Monocular figure-ground segmentation is an important problem in the field of Artificial General Intelligence. A solution to this problem will unlock vast sets of training data, such as Google Images, in which salient objects of interest are situated against complex backgrounds. In order to gain traction on the figure-ground problem we enhanced the Leabra Vision (LVis) model, which is our state-of-the-art model of 3D invariant object recognition [8], such that it can continue to recognize objects against cluttered backgrounds that, while simple, are complex enough to substantially hurt object recognition performance. The principle of operation of the network is that it learns to use a low resolution view of the scene in which high spatial frequency information such as the background falls out of focus in order to predict which aspects of the high resolution scene are the figure. This filtered view then serves to enhance the figure in the input stages of LVis and substantially improves object recognition performance against cluttered backgrounds.