Full text: Proceedings of the Symposium on Global and Environmental Monitoring (Pt. 1)

HE Dong-chen and WANG Li 
Centre d'applications et de recherches en télédétection (CARTEL) 
Université de Sherbrooke 
Sherbrooke, (Québec) 
Edge detection takes an important place in image processing 
and pattern recognition. Its objective is to locate prominent 
edges in an image, and so to separate the components of an 
image into subsets that may correspond to the physical objects 
in the scene. In general, this can be achieved by generating an 
edge map using, for example, edge detection operators or 
some threshold techniques. In most cases, these methods 
assume that at the edge the grey level intensity changes in a 
discontinuous way (usually as a step function). If we need to 
segment a textural scene by finding the texture boundaries, 
traditional methods of edge detection are usually not 
successful sicne they cannot distinguish between the micro 
edges within each texture and the boundaries between 
different textures. One reason for their failure is their inability 
to properly characterize a texture. This problem can be solved 
by combining the traditional edge detection techniques with 
some efficient textural measures. That is, in the edge detection 
operators, grey levels are replaced by textural features. 
Recently, a texture spectrum method has been proposed for 
texture characterization. This paper presents an example of the 
application of the texture spectrum to edge detection. 
Promising results are obtained when locating texture 
boundaries of some of Brodatz' natural images. 
Key Words: Edge detection, Texture boundary, Texture 
analysis, Texture spectrum. 
Texture is the term used to describe the surface of a given 
phenomenon (the spatial intensity relationships between 
pixels) in an image. Texture analysis plays an increasingly 
important role in digital image processing and pattern 
recognition, and is widely applied to the processing and 
interpretation of remotely sensed data, and biomedical and 
microscopic cell images, where texture information is 
sometimes the only way to characterize a digital image. 
One of the major researche works in texture analysis is to 
determine the boundaries between the different textured 
regions for a given image having several textured areas. 
Traditional edge detection operators, such as Roberts, Sobel, 
Prewitt, and Gaussian-Laplacian operators (Marr & Hildreth, 
1980; Young & Fu, 1986), or some threshold techniques 
(Haddon, 1988) are widely used in practice for extracting 
prominent edges in an image. The basic assumption in these 
approaches is that the intensity variation is more or less 
constant within the region and takes a different value at its 
boundary. Due probably to the lack of a formal definition of 
texture and of texture boundaries, these traditional methods of 
edge detection are not suitable for detecting texture boundaries 
in an image. The major difficulty for this kind of approach is 
in distiguishing between the texture boundaries which delimit 
different textures, and the micro-edges located within a same 
This difficulty may be overcome by combining the traditional 
techniques of edge detection with texture features. That is, in 
the edge detection operators, the grey levels of pixels are 
replaced by textural measures. As texture features 
characterize the texture aspects of an image, we may assume 
that these features take more or less a constant value within a 
same textured area, and take a different value at texture 
boundaries. In this way, traditional edge detection can be 
applied to extracting texture boundaries of images. 
In the past years, a lot of features have been proposed to 
extract the texture information of images. They can be broadly 
divided into two categories: structural and statistical 
approaches (Haralick, 1979; He et al. 1987; He et al. 1989). 
In the structural approach, texture is considered as a repetition 
or quasi-repetition of fundamental elements of images with a 
certain rule of displacement, the Fourier spectrum analysis is 
often used to extract the primitives and the displacement rule. 
Texture features are then extracted from the Fourier spectrum 
plan. In the statistical approach, the objective is to characterize 
the stochastic properties of the spatial distribution of grey 
levels in an image. Haralick's features derived from the co 
occurrence matrix approach are widely used in practice 
(Haralick et al., 1973). However in these methods, textures 
are characterized by a set of features. Thus, using only one of 
these features in edge detection operators risks limiting their 
performance in the characterization of textures. 
Recently, the texture spectrum method (He & Wang, 1989; 
Wang & He, 1990) has been proposed as a new statistical 
approach to texture analysis, where the local texture for a 
given pixel and its neighbourhood is characterized by the 
corresponding texture unit, and an image can be characterized 
by its texture spectrum which is the occurrence frequency 
function of all the texture units within the image. This method 
has been used with success for texture classification (Wang & 
He, 1989), textural filtering and for some geological 
applications using remotely sensed data (Wang et al., 1989; 
Wang & He, 1990). These experimental results show the 
promising performance potential of the texture spectrum in the 
characterization of textures. 
In this paper, we present examples of the use of the texture 
spectrum as a texture feature and applying it to texture edge 
This section gives a brief review of the texture unit and 
texture spectrum. The method has been introduced and 
described in detail elsewhere by He and Wang (He & Wang, 
1989; Wang & He, 1990). The basic concept is that a texture 
image can be considered as a set of essential small units 
termed texture units, which characterize the local texture 
information for a given pixel and its neighbourhood. The 
statistics of all the texture units over the whole image reveal 
the global texture aspects.

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