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Visual filling-in for computing perceptual surface properties.

H Neumann1, L Pessoa, T Hansen

  • 1Universität Ulm, Fakultät für Informatik, Abteilung Neuroinformatik, Oberer Eselsberg, Germany.

Biological Cybernetics
|November 28, 2001
PubMed
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Visual system brightness perception is explained by filling-in processes. A new confidence-based filling-in model offers more robust brightness representations by regularizing sparse visual data.

Area of Science:

  • Computational neuroscience
  • Visual perception
  • Mathematical modeling

Background:

  • The visual system integrates local signals into global representations.
  • Generating global brightness perception from sparse local measurements is a key challenge.
  • Existing models, like Cohen and Grossberg's, describe brightness filling-in mechanisms.

Purpose of the Study:

  • To investigate the computational problem of generating brightness representations from sparse visual data.
  • To demonstrate how filling-in from contrast estimates provides a regularized solution.
  • To propose a novel, more robust filling-in mechanism for brightness perception.

Main Methods:

  • Modeling brightness filling-in processes using contrast estimates.
  • Applying regularization theory to visual perception problems.

Related Experiment Videos

  • Utilizing linear spatially variant diffusion frameworks.
  • Developing a modified filling-in approach termed confidence-based filling-in.

    • Main Results:

    • Filling-in from contrast estimates yields a regularized solution for brightness computation.
    • The study provides deeper insights into the objective functions underlying filling-in processes.
    • Confidence-based filling-in generates more robust brightness representations compared to existing models.

    Conclusions:

    • The research connects biological vision modeling with mathematical regularization and diffusion theory.
    • This work unifies disparate research directions in visual perception and computational neuroscience.
    • The proposed confidence-based filling-in offers a more robust approach to modeling brightness perception.