[Comp-neuro] a neural model of 3D shape-from-tenure
steve at cns.bu.edu
Sat Oct 28 23:01:05 CEST 2006
The following article is now available at
Grossberg, S., Kuhlmann, L., and Mingolla, E.
A Neural Model of 3D Shape-From-Texture:
Multiple-Scale Filtering, Boundary Grouping, and Surface Filling-In
Vision Research, in press
A neural model is presented of how cortical areas V1, V2, and V4
interact to convert a textured 2D image into a representation of
curved 3D shape. Two basic problems are solved to achieve this: (1)
Patterns of spatially discrete 2D texture elements are transformed
into a spatially smooth surface representation of 3D shape. (2)
Changes in the statistical properties of texture elements across
space induce the perceived 3D shape of this surface representation.
This is achieved in the model through multiple-scale filtering of a
2D image, followed by a cooperative-competitive grouping network that
coherently binds texture elements into boundary webs at the
appropriate depths using a scale-to-depth map and a subsequent depth
competition stage. These boundary webs then gate filling-in of
surface lightness signals in order to form a smooth 3D surface
percept. The model quantitatively simulates challenging
psychophysical data about perception of prolate ellipsoids (Todd and
Akerstrom, 1987, J. Exp. Psych., 13, 242). In particular, the model
represents a high degree of 3D curvature for a certain class of
images, all of whose texture elements have the same degree of optical
compression, in accordance with percepts of human observers.
Simulations of 3D percepts of an elliptical cylinder, a slanted
plane, and a photo of a golf ball are also presented.
Key words: shape, texture, neural modeling, 3D vision, visual cortex,
FACADE model, BCS, FCS, multiple scales, perceptual grouping,
size-disparity correlation, filling-in, shape-from-texture.
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