Determination of major maceral groups in coal by automated image analysis procedures

Jamshid Dehmeshki, M. Farhang Daemi, N. J. Miles, B. P. Atkin, R. E. Marston

    Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

    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.

    Original languageEnglish
    Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
    EditorsDavid P. Casasent
    Pages62-73
    Number of pages12
    Publication statusPublished - 1995
    EventIntelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling - Philadelphia, PA, USA
    Duration: 23 Oct 199526 Oct 1995

    Publication series

    NameProceedings of SPIE - The International Society for Optical Engineering
    Volume2588
    ISSN (Print)0277-786X

    Conference

    ConferenceIntelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
    CityPhiladelphia, PA, USA
    Period23/10/9526/10/95

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering

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