Image Processing-based Quantitative Representation of Coal Porosity
Abstract
As CFE-SEM fails to quantitatively represent coal porosity, we initiated a novel technique by using image sensing. First, an analysis was conducted on the grey level distribution of pixels of coal porosity images shot by CFE-SEM. According to the result, pore area signals are less bright and lower in pixel grey levels than signals elsewhere. Then, we converted original images into binary images in Matlab to calculate binary pixels. Image porosity is obtained by dividing the total number of pixels by binary pixels. The identity between image porosity computed by the proposed method and the one by liquid nitrogen adsorption technique is proved to be over 81%. In this way, this approach provides a new thought to coal porosity representation.
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