Raw coal ore classification using image segmentation methods

The research done in order to complete this dissertation can be summarised as the investigation, implementation, analysis, and comparison of data analysis techniques with the intention to segment a digital image of a raw coal sample into its constituent materials. The goal is to obtain a per-pixel classification of the image with a sufficient level of accuracy, to be used to generate a viable washability curve. An extensive literature survey was done in order to investigate the current state of the applicable fields of image segmentation, data classification, clustering, and machine learning. The identified techniques that are both pertinent and suitable to the problem defined above were then implemented in a common development environment. In order to investigate, rate, and compare the techniques they were analysed using internal analysis techniques as well as compared to ground truth classifications obtained by expert geologists. The research is based on previous work [1], and the explicit goal is to improve an existing system of image classification. The existing system makes use of feature extraction and a clustering algorithm, called k-means, to identify similar groups of pixels and then assign them to model values that are selected by a user. The results of the research presented here makes use of a system using a similar high level topology with several alterations that were made to improve the accuracy of the image segmentation. The most prominent of these alterations is the use of a mean shift algorithm to "group" the pixels together and assign them to the models. This choice was, largely, made due to the higher level of spatial information about the image pixels that is incorporated in the mean shift algorithm. This is as opposed to the k-means algorithm that only makes use of range values. It was found that image segmentation techniques can, indeed, be used to achieve a sufficient level of accuracy towards raw coal ore classification under certain conditions. The success of each technique, and the constraints under which they achieve it, are identified and thoroughly motivated in this dissertation. The first half of the dissertation investigates the research problem, existing system, and possible alternatives in the literature. The second half presents the results of the research and discusses the knowledge obtained from it.