Abstract.
The present paper presents a model for
texture discrimination based on Gabor functions. In
this model the Gabor power spectrum of the micropatterns
corresponding to different textures is calculated.
A function that measures the difference between
the spectrum of two micropatterns is introduced
and its values are correlated with human performance
in preattentive detection tasks. In addition, a two stage
algorithm for texture segregation is presented. In the
first stage the input image is transformed via Gabor
filters into a representation image that allows discrimination
between features by means of intensity
differences. In the second stage the borders between
areas of different textures are found using a Laplacian
of Gaussian operator. This algorithm is sensitive to
energy differences, rotation and spatial frequency and
is insensitive to local translation. The model was tested
by means of several simulations and was found to be in
good correlation with known psychophysical characteristics
as texton based texture segregation and micropattern
density sensitivity. However, this simple model
fails to predict human performance in discrimination
tasks based on differences in the density of "terminators".
In this case human performance is better
than expected.
1 Introduction
In a number of recent papers (Julesz 1984a, 1986;
Treisman 1986) it has been suggested that visual
perception operates in two modes, one preattentive
and the other attentive. In the preattentive mode
(which is parallel), differences in a few local structural
features are detected over the entire visual field. In the
attentive mode (which is sequential), a serial search
over a narrow aperture of attention is performed in fast
steps (20-80ms). Only in this mode recognition is
possible. According to the above mentioned works
texture segregation in the preattentive mode is based
on local feature differences in the picture. Psychophysical
experiments (Beck 1972, 1983; Julesz 1975, 1981,
1986) have shown that texture discrimination in the
preattentive mode depends mainly on some simple
properties such as brightness, color, size and slope (of
lines, blobs) of the basic texture elements that compose
the picture.

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