A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
The segmentation of rock grains on images depicting bulk rock materials is considered. The rocks material images are transformed by selected texture operators, to obtain a set of features describing them. The first order features, second-order features, run-length matrix, grey tone difference matrix, and Laws' energies are used for that purpose. The features are classified using k-nearest neighbours, support vector machines, and artificial neural networks classifiers. The results show that thedoi:10.5566/ias.2186 fatcat:ibjmwuxszjea3mmqxywyc5zmz4