TY - GEN
T1 - Determination of major maceral groups in coal by automated image analysis procedures
AU - Dehmeshki, Jamshid
AU - Daemi, M. Farhang
AU - Miles, N. J.
AU - Atkin, B. P.
AU - Marston, R. E.
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 1995
Y1 - 1995
N2 - This paper describes development of an automated and efficient system for classifying of different major maceral groups within polished coal blocks. Coal utilization processes can be significantly affected by the distribution of macerals in the feed coal. In carbonization, for example, maceral group analysis is an important parameter in determining the correct coal blend to produce the required coking properties. In coal liquefaction, liptinites and vitrinites convert more easily to give useful products than inertinites. Microscopic images of coal are inherently difficult to interpret by conventional image processing techniques since certain macerals show similar visual characteristics. It is particularly difficult to distinguish between the liptinite maceral and the supporting setting resin. This requires the use of high level image processing as well as fluorescence microscopy in conjunction with normal white light microscopy. This paper is concerned with two main stages of the work, namely segmentation and interpretation. In the segmentation stage, a cooperative, iterative approach to segmentation and model parameter estimation is defined which is a stochastic variant of the Expectation Maximization algorithm. Because of the high resolution of these images under study, the pixel size is significantly smaller than the size of most of the different regions of interest. Consequently adjacent pixels are likely to have similar labels. In our Stochastic Expectation Maximization method the idea that neighboring pixels are similar to one another is expressed by using Gibbs distribution for the priori distribution of regions (labels). We also present a suitable statistical model for distribution of pixel values within each region. In the interpretation stage, the coal macerals are identified according to the measurement information on the segmented region and domain knowledge. Studies show that the system is able to distinguish coals macerals, especially Fusinite from Pyrite or liptinite from mineral which previous attempts have been unable to resolve.
AB - This paper describes development of an automated and efficient system for classifying of different major maceral groups within polished coal blocks. Coal utilization processes can be significantly affected by the distribution of macerals in the feed coal. In carbonization, for example, maceral group analysis is an important parameter in determining the correct coal blend to produce the required coking properties. In coal liquefaction, liptinites and vitrinites convert more easily to give useful products than inertinites. Microscopic images of coal are inherently difficult to interpret by conventional image processing techniques since certain macerals show similar visual characteristics. It is particularly difficult to distinguish between the liptinite maceral and the supporting setting resin. This requires the use of high level image processing as well as fluorescence microscopy in conjunction with normal white light microscopy. This paper is concerned with two main stages of the work, namely segmentation and interpretation. In the segmentation stage, a cooperative, iterative approach to segmentation and model parameter estimation is defined which is a stochastic variant of the Expectation Maximization algorithm. Because of the high resolution of these images under study, the pixel size is significantly smaller than the size of most of the different regions of interest. Consequently adjacent pixels are likely to have similar labels. In our Stochastic Expectation Maximization method the idea that neighboring pixels are similar to one another is expressed by using Gibbs distribution for the priori distribution of regions (labels). We also present a suitable statistical model for distribution of pixel values within each region. In the interpretation stage, the coal macerals are identified according to the measurement information on the segmented region and domain knowledge. Studies show that the system is able to distinguish coals macerals, especially Fusinite from Pyrite or liptinite from mineral which previous attempts have been unable to resolve.
UR - http://www.scopus.com/inward/record.url?scp=0029480992&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029480992
SN - 0819419524
SN - 9780819419521
T3 - Proceedings of SPIE - The International Society for Optical Engineering
SP - 62
EP - 73
BT - Proceedings of SPIE - The International Society for Optical Engineering
A2 - Casasent, David P.
T2 - Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
Y2 - 23 October 1995 through 26 October 1995
ER -