An adaptive estimation and segmentation technique for determination of major maceral groups in coal
This paper describes the development of an automated image based system for the classification of macerals in polished coal blocks. Coal petrology, and especially the estimation of the maceral content of a coal, has traditionally been considered to be a highly skilled and time consuming operation. However the recent upsurge in interest in this subject, driven by environmental legislation related to the utilisation of coal, has necessitated the development of a reliable automated system for maceral analysis. Manual maceral analysis is time consuming and its accuracy is largely dependent upon the skill of the operator. The major drawbacks to manual maceral analysis are related to time and operator fatigue, which can develop after the analysis of only one or two polished blocks. The reproducibility of the results from manual maceral analysis is also dependent upon the experience of the operator.
In this paper, a cooperative, iterative approach to segmentation and model parameter estimation is defined which is a stochastic variant of the Expectation Maximization (EM) 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 (SEM) method the idea that neighboring pixels arc 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 maceral groups. This paper illustrate the power of the SEM method for the segmentation of macerals types.
Key WordsMacerals Coal Segmentation Expectation maximization Gibbs distribution Stochastic Model
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