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Proceedings Paper

Identification of quality of coal using an automated image analysis system
Author(s): Jamshid Dehmeshki; Mohammad Farhang Daemi; N. J. Miles; B. P. Atkin
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Paper Abstract

This paper is concerned with development of an automated and efficient system for quality control of coal. This is achieved by distinguishing between different major maceral groups present in the polished coal blocks when viewed under a microscope. Coal utilization processes can be significantly affected by the distribution of macerals in the feed coal. Manual petrographic analysis of coal requires a highly skilled operator and the results obtained can have a high degree of subjectivity. One way of overcoming these problems is to employ automated image analysis. The system described here consists of two stages: segmentation and interpretation. In the segmentation stage, the aim is to partition the images into different types of macerals. We have implemented a multi-scale segmentation technique in which the result of each process at a given resolution is used to adjust the other process at the next resolution. This approach combines a suitable statistical model for distribution of pixel values within each macerals group and a transition distribution from coarse to fine scale, based on a son-father relationship, which is defined between the nodes in adjacent levels. At each level, segmentation is performed by maximizing the a posteriori probability (MAP) which is achieved by a relaxation algorithm, similar to Besegs work. There are two major reasons for carrying out the segmentation estimation over a hierarchy of resolutions: to speed up the estimation process, and to incorporate large scale characteristics of each pixel. The speed can be further improved by restricting the operation on the pixels which are introduced as mixed in each resolution, by which the number of pixels to be considered are significantly reduced. In the interpretation stage, the coal macerals are identified according to the measurement information on the segmented region and domain knowledge. The paper describes the knowledge base used in this application in some detail. The system has been particularly successful in correctly classifying difficult cases, such as liptinite, vitrinite, semi-fusinite and pyrite.

Paper Details

Date Published: 21 February 1996
PDF: 12 pages
Proc. SPIE 2665, Machine Vision Applications in Industrial Inspection IV, (21 February 1996); doi: 10.1117/12.232247
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)

Published in SPIE Proceedings Vol. 2665:
Machine Vision Applications in Industrial Inspection IV
A. Ravishankar Rao; Ning Chang, Editor(s)

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