Connectionists: a neural model of 3D shape-from-tenure

[Stephen Grossberg]

The following article is now available at 
http://www.cns.bu.edu/Profiles/Grossberg :

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

ABSTRACT
  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.