Paper available on Visual Segmentation by intracortical interactions in V1

[Dr Zhaoping Li]

Paper  on  Visual Segmentation by intracortical interactions in V1

Published in 
NETWORK: COMPUTATION IN NEURAL SYSTEMS, vol.10, number 2, May 1999, page, 187-212

Available at Online Service of the Journal  --- http://www.iop.org/EJ/S

Or on the website --- http://www.gatsby.ucl.ac.uk/~zhaoping/preattentivevision.html


       
Title: Visual segmentation by contextual influences via
       intracortical interactions in  primary visual cortex.

Author: Zhaoping LI

Abstract:

Stimuli outside classical receptive fields have been shown to
exert significant influence over the activities of neurons
in  primary visual cortex.  We propose that contextual influences
are used for pre-attentive visual segmentation.  The difference between
contextual influences near and far from region boundaries makes
neural activities near region boundaries higher than elsewhere, making
boundaries more salient for perceptual pop-out.  The cortex thus
computes {\it global} region boundaries by detecting the breakdown of 
homogeneity or translation invariance in the input, using  
{\it local} intra-cortical interactions mediated by the horizontal connections.
This proposal is implemented in a biologically based model of V1,
and demonstrated using examples of texture segmentation and 
figure-ground segregation.  The model is also the first that
performs texture or region segmentation in exactly the same neural circuit
that solves the dual problem of the enhancement of
contours, as is suggested by experimental observations.
The computational framework in this model is simpler than previous
approaches, making it implementable by V1 mechanisms, though  higher level
visual mechanisms are needed to refine its output.  However, it easily handles
a class of segmentation problems that are known to be tricky.
Its behavior is compared with psychophysical and physiological
data on segmentation, contour enhancement, contextual influences, and
other phenomena such as asymmetry in visual search.