Classification of coal images by a multi-scale segmentation techniques

This paper describes development of an automated and efficient technique for classifying different major maceral groups within polished coal blocks. Coal utilisation processes can be significantly affected by the distribution of macerals in the feed coal. Classical manual maceral analysis requires a highly skilled operator and the time to perform an analysis can depend on the complexity of the sample and on the work load of the operator. Also if different operators are employed lower levels of reproducibility may result. The aim of segmentation is to partition the images into different types of macerals. A multi-scale approach to segmentation is defined 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. This transition function is based on the idea that neighboring pixels are similar to one another. This is mainly true due to the high resolution of these images understudy, which means that the pixel size is significantly smaller than the size of most of the different regions of interest