Haralick Texture Feature

Topics: Image processing, Computer vision, Intel Core 2 Pages: 60 (16469 words) Published: June 17, 2013
Computer Engineering
Mekelweg 4, 2628 CD Delft The Netherlands http://ce.et.tudelft.nl/

2010

MSc THESIS
Optimization of Texture Feature Extraction Algorithm
Tuan Anh Pham Abstract
Texture, the pattern of information or arrangement of the structure found in an image, is an important feature of many image types. In a general sense, texture refers to surface characteristics and appearance of an object given by the size, shape, density, arrangement, proportion of its elementary parts. Due to the signification of texture information, texture feature extraction is a key function in various image processing applications, remote sensing and contentbased image retrieval. Texture features can be extracted in several methods, using statistical, structural, model-based and transform information, in which the most common way is using the Gray Level Co-occurrence Matrix (GLCM). GLCM contains the second-order statistical information of spatial relationship of pixels of an image. From GLCM, many useful textural properties can be calculated to expose details about the image content. However, the calculation of GLCM is very computationally intensive and time consuming. In this thesis, the optimizations in the calculation of GLCM and texture features are considered, different approaches to the structure CE-MS-2010-21 of GLCM are compared. We also proposed parallel computing of GLCM and texture features using Cell Broadband Engine Architecture (Cell Processor). Experimental results show that our parallel approach reduces impressively the execution time for the GLCM texture feature extraction algorithm.

Faculty of Electrical Engineering, Mathematics and Computer Science

Optimization of Texture Feature Extraction Algorithm
THESIS

submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in

COMPUTER ENGINEERING
by

Tuan Anh Pham born in Hanoi, Vietnam

Computer Engineering Department of Electrical Engineering Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology

Optimization of Texture Feature Extraction Algorithm
by Tuan Anh Pham Abstract
exture, the pattern of information or arrangement of the structure found in an image, is an important feature of many image types. In a general sense, texture refers to surface characteristics and appearance of an object given by the size, shape, density, arrangement, proportion of its elementary parts. Due to the signification of texture information, texture feature extraction is a key function in various image processing applications, remote sensing and contentbased image retrieval. Texture features can be extracted in several methods, using statistical, structural, model-based and transform information, in which the most common way is using the Gray Level Co-occurrence Matrix (GLCM). GLCM contains the second-order statistical information of spatial relationship of pixels of an image. From GLCM, many useful textural properties can be calculated to expose details about the image content. However, the calculation of GLCM is very computationally intensive and time consuming. In this thesis, the optimizations in the calculation of GLCM and texture features are considered, different approaches to the structure of GLCM are compared. We also proposed parallel computing of GLCM and texture features using Cell Broadband Engine Architecture (Cell Processor). Experimental results show that our parallel approach reduces impressively the execution time for the GLCM texture feature extraction algorithm.

T

Laboratory Codenumber Committee Members Advisor: Advisor: Chairperson: Member: Member: Member:

: : :

Computer Engineering CE-MS-2010-21

Dr. Koen Bertels, CE, TU Delft

Dr. Asadollah Shahbahrami, CE, TU Delft

Dr. Koen Bertels, CE, TU Delft

Dr. Koen Bertels, CE, TU Delft

Dr. Ir. Zaid Al-Ars , CE, TU Delft

Dr. Todor Stefanov, Leiden University

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Bibliography: Tuan Anh Pham Delft, The Netherlands July 29, 2010
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