期刊
COMPUTERS IN INDUSTRY
卷 75, 期 -, 页码 35-45出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.compind.2015.10.003
关键词
Coal petrology; Coal image analysis; Coal texture; Electron microscopy; Pattern recognition
Increased coal utilization has accelerated the need of understanding the basic knowledge of coal quality. Coal is highly heterogeneous in nature and because of its heterogeneity, numerous analytical techniques are needed for its characterization so as to predict its behavior and characteristics. Conventional analysis had been a basic technique long since for coal characterization performed by petrologists. Such conventional characterization of coal samples is time consuming and are limited by the high degree of subjectivity in the results. This paper come up with an automated image analysis approach towards the characterization of given different grades of coal samples. The objective of this work is to improve the characterization of coal samples by analyzing the textural and color features of coal using image processing techniques and to assist in the development of a preliminary screening of the coal samples. Automated characterization of coal is accomplished using image acquisition, features extraction, feature selection and classification over scanned electron microscopic images of coal samples. Hence, authentic and accurate subtyping of coal is obtained with the use of improved prominent features and a standard neural network classifier. (C) 2015 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据