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    A Contrast- and Luminance-Driven Multiscale Netowrk Model of Brightness Perception

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    Date Issued
    1994-05
    Author(s)
    Pessoa, Luiz
    Mingolla, Ennio
    Neumann, Heiko
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    Permanent Link
    https://hdl.handle.net/2144/2155
    Abstract
    A neural network model of brightness perception is developed to account for a wide variety of data, including the classical phenomenon of Mach bands, low- and high-contrast missing fundamental, luminance staircases, and non-linear contrast effects associated with sinusoidal waveforms. The model builds upon previous work on filling-in models that produce brightness profiles through the interaction of boundary and feature signals. Boundary computations that are sensitive to luminance steps and to continuous lumi- nance gradients are presented. A new interpretation of feature signals through the explicit representation of contrast-driven and luminance-driven information is provided and directly addresses the issue of brightness "anchoring." Computer simulations illustrate the model's competencies.
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    Copyright 1994 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.
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    • CAS/CNS Technical Reports [485]


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