Share Email Print

Proceedings Paper

Determination of major maceral groups in coal by automated image analysis procedures
Author(s): Jamshid Dehmeshki; Mohammad Farhang Daemi; N. J. Miles; B. P. Atkin; R. E. Marston
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 3 October 1995
PDF: 12 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222724
Show Author Affiliations
Jamshid Dehmeshki, Univ. of Nottingham (United Kingdom)
Mohammad Farhang Daemi, Univ. of Nottingham (United Kingdom)
N. J. Miles, Univ. of Nottingham (United Kingdom)
B. P. Atkin, Univ. of Nottingham (United Kingdom)
R. E. Marston, Univ. of Nottingham (United Kingdom)

Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top