Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [WP-1 ADVANCED COMPUTING FOR INTERPRETATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: DETECTING TEXTURE EDGES FROM IMAGES. HE Dong-chen and WANG Li

Document type:: Multivolume work

Structure type:: Chapter

DETECTING TEXTURE EDGES FROM IMAGES 329 HE Dong-chen and WANG Li Centre d'applications et de recherches en télédétection (CARTEL) Université de Sherbrooke Sherbrooke, (Québec) CANADA J1K 2R1 ABSTRACT 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. 1 INTRODUCTION 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 texture. 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 detection. 2 TEXTURE UNITS & TEXTURE SPECTRUM 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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