Texture segregation, surface representation and figure-ground separation.
A widespread view is that most texture segregation can be accounted for by differences in the spatial frequency content of texture regions. Evidence from both psychophysical and physiological studies indicate, however, that beyond these early filtering stages, there are stages of 3-D boundary segmentation and surface representation that are used to segregate textures. Chromatic segregation of element-arrangement patterns--as studied by Beck and colleagues--cannot be completely explained by the filtering mechanisms previously employed to account for achromatic segregation. An element arrangement pattern is composed of two types of elements that are arranged differently in different image regions (e.g. vertically on top and diagonally on the bottom). FACADE theory mechanisms that have previously been used to explain data about 3-D vision and figure-ground separation are here used to simulate chromatic texture segregation data, including data with equiluminant elements on dark or light homogeneous backgrounds, or backgrounds composed of vertical and horizontal dark or light stripes, or horizontal notched stripes. These data include the fact that segregation of patterns composed of red and blue squares decreases with increasing luminance of the interspaces. Asymmetric segregation properties under 3-D viewing conditions with the equiluminant elements close or far are also simulated. Two key model properties are a spatial impenetrability property that inhibits boundary grouping across regions with non-collinear texture elements and a boundary-surface consistency property that uses feedback between boundary and surface representations to eliminate spurious boundary groupings and separate figures from their backgrounds.
Grossberg
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S
Department of Cognitive and Neural Systems and Center for Adaptive Systems, Boston University, 677 Beacon Street, Boston, MA 02215, USA. steve@cns.bu.edu
Pessoa
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L
D003118:Color Perception / Q000502:physiology*; D019295:Computational Biology; D003867:Depth Perception / Q000502:physiology*; D006801:Humans; D010364:Pattern Recognition, Visual / Q000502:physiology*