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Research article summary (published 30 Dec 2005):

A neural model of surface perception: lightness, anchoring, and filling-in.

Full Abstract

A neural model is proposed of how the visual system processes natural images under variable illumination conditions to generate surface lightness percepts. Previous models clarify how the brain can compute relative contrast. The anchored Filling-In Lightness Model (aFILM) clarifies how the brain 'anchors' lightness percepts to determine an absolute lightness scale that uses the full dynamic range of neurons. The model quantitatively simulates lightness anchoring properties (Articulation, Insulation, Configuration, Area Effect) and other lightness data (discounting the illuminant, the double brilliant illusion, lightness constancy and contrast, Mondrian contrast constancy, Craik-O'Brien-Cornsweet illusion). The model clarifies how retinal processing stages achieve light adaptation and spatial contrast adaptation, and how cortical processing stages fill-in surface lightness using long-range horizontal connections that are gated by boundary signals. The new filling-in mechanism runs 1000 times faster than diffusion mechanisms of previous filling-in models.

 

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Author information

Author/s: Grossberg, Stephen (S); Hong, Simon (S);

Affiliation: Department of Cognitive and Neural Systems and Center for Adaptive Systems, Boston University, 677 Beacon Street, Boston, MA 02215, USA. steve(-atsign-)bu.edu

Journal and publication information

Publication Type: Comparative Study; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.

Journal: Spatial vision (Spat Vis), published in Netherlands. (Language: eng)

Reference: 2006-; vol 19 (issue 2-4) : pp 263-321

Dates: Created 2006/07/25; Completed 2006/08/10; Revised 2006/11/15;

PMID: 16862842, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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