08-10-2012, 11:39 AM
Texture Analysis
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This chapter reviews and discusses various aspects of texture analysis. The concentration is on
the various methods of extracting textural features from images. The geometric, random field, fractal,
and signal processing models of texture are presented. The major classes of texture processing problems
such as segmentation, classification, and shape from texture are discussed. The possible application
areas of texture such as automated inspection, document processing, and remote sensing are
summarized. A bibliography is provided at the end for further reading.
Introduction
In many machine vision and image processing algorithms, simplifying assumptions are
made about the uniformity of intensities in local image regions. However, images of real
objects often do not exhibit regions of uniform intensities. For example, the image of a
wooden surface is not uniform but contains variations of intensities which form certain
repeated patterns called visual texture. The patterns can be the result of physical surface
properties such as roughness or oriented strands which often have a tactile quality, or they
could be the result of reflectance differences such as the color on a surface.
We recognize texture when we see it but it is very difficult to define. This difficulty is demonstrated
by the number of different texture definitions attempted by vision researchers.
Coggins [1] has compiled a catalogue of texture definitions in the computer vision literature
and we give some examples here.
Motivation
Texture analysis is an important and useful area of study in machine vision. Most natural
surfaces exhibit texture and a successful vision system must be able to deal with the textured
world surrounding it. This section will review the importance of texture perception
from two viewpoints — from the viewpoint of human vision or psychophysics and from
the viewpoint of practical machine vision applications.
Psychophysics
The detection of a tiger among the foliage is a perceptual task that carries life and death
consequences for someone trying to survive in the forest. The success of the tiger in camouflaging
itself is a failure of the visual system observing it. The failure is in not being
able to separate figure from ground. Figure-ground separation is an issue which is of
intense interest to psychophysicists. The figure-ground separation can be based on various
cues such as brightness, form, color, texture, etc. In the example of the tiger in the forest,
texture plays a major role. The camouflage is successful because the visual system of the
observer is unable to discriminate (or segment) the two textures of the foliage and the tiger
skin. What are the visual processes that allow one to separate figure from ground using the
texture cue? This question is the basic motivation among psychologists for studying texture
perception.
Another reason why it is important to study the psychophysics of texture perception is that
the performance of various texture algorithms is evaluated against the performance of the
human visual system doing the same task. For example, consider the texture pair in
Figure 4(a), first described by Julesz [11]. The image consists of two regions each of
which is made up of different texture tokens. Close scrutiny of the texture image will indicate
this fact to the human observer. The immediate perception of the image.
Applications
Texture analysis methods have been utilized in a variety of application domains. In some
of the mature domains (such as remote sensing) texture already has played a major role,
while in other disciplines (such as surface inspection) new applications of texture are
being found. We will briefly review the role of texture in automated inspection, medical
image processing, document processing, and remote sensing. Images from two application
domains are shown in Figure 5. The role that texture plays in these examples varies
depending upon the application. For example, in the SAR images of Figures 5(b) and ©
texture is defined to be the local scene heterogeneity and this property is used for classification
of land use categories such as water, agricultural areas, etc. In the ultrasound image
of the heart in Figure 5(a), texture is defined as the amount of randomness which has a
lower value in the vicinity of the border between the heart cavity and the inner wall than in
the blood filled cavity. This fact can be used to perform segmentation and boundary detection
using texture analysis methods.
Medical Image Analysis
Image analysis techniques have played an important role in several medical applications.
In general, the applications involve the automatic extraction of features from the image
which are then used for a variety of classification tasks, such as distinguishing normal tissue
from abnormal tissue. Depending upon the particular classification task, the extracted
features capture morphological properties, color properties, or certain textural properties
of the image.
The textural properties computed are closely related to the application domain to be used.
For example, Sutton and Hall [27] discuss the classification of pulmonary disease using
texture features. Some diseases, such as interstitial fibrosis, affect the lungs in such a manner
that the resulting changes in the X-ray images are texture changes as opposed to
clearly delineated lesions. In such applications, texture analysis methods are ideally suited
for these images. Sutton and Hall propose the use of three types of texture features to distinguish
normal lungs from diseased lungs. These features are computed based on an isotropic
contrast measure, a directional contrast measure, and a Fourier domain energy
sampling. In their classification experiments, the best classification results were obtained
using the directional contrast measure.
Remote Sensing
Texture analysis has been extensively used to classify remotely sensed images. Land use
classification where homogeneous regions with different types of terrains (such as wheat,
bodies of water, urban regions, etc.) need to be identified is an important application.
Haralick et al. [41] used gray level co-occurrence features to analyze remotely sensed
images. They computed gray level co-occurrence matrices for a distance of one with four
directions ( , , , and ). For a seven-class classification problem, they
obtained approximately 80% classification accuracy using texture features.
Rignot and Kwok [42] have analyzed SAR images using texture features computed from
gray level co-occurrence matrices. However, they supplement these features with knowledge
about the properties of SAR images. For example, image restoration algorithms were
used to eliminate the specular noise present in SAR images in order to improve classification
results. The use of various texture features was studied for analyzing SAR images by
Schistad and Jain [43]. SAR images shown in Figures 5(b) and © were used to identify
land use categories of water, agricultural areas, urban areas, and other areas. Fractal
dimension, autoregressive Markov random field model, and gray level co-occurrence texture
features were used in the classification. The classification errors ranged from 25% for
the fractal based models to as low as 6% for the MRF features. Du [44] used texture features
derived from Gabor filters to segment SAR images. He successfully segmented the
SAR images into categories of water, new forming ice, older ice, and multi-year ice. Lee
and Philpot [45] also used spectral texture features to segment SAR images.
Geometrical Methods
The class of texture analysis methods that falls under the heading of geometrical methods
is characterized by their definition of texture as being composed of “texture elements” or
primitives. The method of analysis usually depends upon the geometric properties of these
texture elements. Once the texture elements are identified in the image, there are two
major approaches to analyzing the texture. One computes statistical properties from the
extracted texture elements and utilizes these as texture features. The other tries to extract
the placement rule that describes the texture. The latter approach may involve geometric
or syntactic methods of analyzing texture.