NDLI: Optical textures classification of coke microscopic image based on SVM

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Optical textures classification of coke microscopic image based on SVM

Content Provider	IEEE Xplore Digital Library
Author	Peizhen Wang Ke Zhou Fang Zhou Dailin Zhang
Copyright Year	2010
Description	Author affiliation: School of Electrical & Information, Anhui University of Technology, Ma'anshan, China (Peizhen Wang; Ke Zhou; Fang Zhou) \|\| Anhui Key Laboratory of clean conversion and utlization, Anhui University of Technology, Ma'anshan, China (Dailin Zhang)
Abstract	In the view of characteristics of coke optical texture in micrograph, a classification method, which is based on Support Vector Machine and combining color and texture features, is proposed. Firstly, color features of the coke microscopic images of different optical textures are analyzed. With color features, isotropic and anisotropic components are classified. Then the gray level co-occurrence matrix of anisotropic components is calculated, the texture features (such as entropy) of each anisotropic components are computed. With texture features, each subclass in anisotropic component is further classified. Experimental results show that with the proposed method the classification among different optical textures of coke is more reasonable and effective than traditional techniques, including neural networks.
File Size	1101248
File Format	PDF
ISBN	9781424472352
e-ISBN	9781424472376
DOI	10.1109/ICCASM.2010.5619433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Support vector machine classification Micrograph Optical imaging Fractals Texture feature Coke optical texture Support Vector Machine Bonding Color feature
Content Type	Text
Resource Type	Article

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