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STATISTICAL AND STRUCTURAL APPROACH TO TEXTURE
By
R.M.Haralick
Department of Electrical Engineering, Department of Computer
Science, The University of Kansas, Lawrence, Kansas 66045, USA
ABSTRACT
In this survey we review the image processing literature on the
various approaches and models investigators have used for texture.
These include statistical approaches of autocorrelation function,
optical transforms, digital transforms, textural edgeness, struc-
tural element, gray tone co-occurrence, run lengths, and auto-
regressive models. We discuss and generalize some structural
approaches to texture based on more complex primitives than gray
tone. We conclude with some structural-statistical generalizations
which apply the statistical techniques to the structural primitives.
1.0 INTRODUCTION
Texture is an important characteristic for the analysis of many
types of images. It can be seen in all images from multi-spectral
scanner images obtained from aircraft or satellite platforms
(which the remote sensing community analyzes) to microscopic
images of cell cultures or tissue samples (which the bio-medical
community analyzes). Despite its important and ubiquity in image
data, a formal approach or precise definition of texture does
not exist. The texture discrimination techniques are, for the
most part, ad-hoc. In this paper we survey, unify, and generalize
some of the extraction techniques and models which investigators
have been using to measure textural properties.
The image texture we consider is non-figurative and cellular. We
think of this kind of texture as an organized area phenomena. When
it is decomposable, it has two basic dimensions on which it may
be described. The first dimension is for describing the primitives
out of which the image texture is composed, and the second dimen-
sion is for the description of the spatial dependence or inter-
action between the primitives of an image texture. The first
dimension is concerned with tonal primitives or local properties,
and the second dimension is concerned with the spatial organization
of the tonal primitives.
Tonal primitives are regions with tonal properties. The tonal
primitive can be described in terms such as the average tone, or
maximum and minimum tone of its region. The region is a maximally
connected set of pixels having a given tonal property. The tonal
region can be evaluated in terms of its area and shape. The tonal
primitive includes both its gray tone and tonal region properties.
An image texture is described by the number and types of its
primitives and the spatial organization or layout of its primitives.
The spatial organization may be random, may have a pairwise de-