We previously proposed a physiologically realistic model for stereo vi
sion based on the quantitative binocular receptive field profiles mapp
ed by Freeman and coworkers, Here we present several new results about
the model that shed light on the physiological processes involved in
disparity computation, First, we show that our model can be extended t
o a much more general class of receptive field profiles than the commo
nly used Gabor functions, Second, we demonstrate that there is, howeve
r, an advantage of using the Gabor filters: similar to our perception,
the stereo algorithm with the Gabor filters has a small bias towards
zero disparity, Third, we prove that the complex cells as described by
Freeman et al, compute disparity by effectively summing up two relate
d cross products between the band-pass filtered left and right retinal
image patches, This operation is related to cross-correlation but it
overcomes some major problems with the standard correlator. Fourth, we
demonstrate that as few as two complex cells at each spatial location
are sufficient for a reasonable estimation of binocular disparity, Fi
fth, we find that our model can be significantly improved by consideri
ng the fact that complex cell receptive fields are, on average, larger
than those of simple cells, This fact is incorporated into the model
by averaging over several quadrature pairs of simple cells with nearby
and overlapping receptive fields to construct a model complex cell, T
he disparity tuning curve of the resulting complex cell is much more r
eliable than that constructed from a single quadrature pair of simple
cells used previously, and the computed disparity maps for random dot
stereograms with the new algorithm are very similar to human perceptio
n, with sharp transitions at disparity boundaries, Finally, we show th
at under most circumstances our algorithm works equally well with eith
er of the two well-known receptive field models in the literature. (C)
1997 Elsevier Science Ltd.