Author:
Nurzynska Karolina,Iwaszenko Sebastian
Abstract
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 the border of rocks grains can be determined with above 70% accuracy. The multi-texture approach was also investigated, leading to an increase in accuracy to over 77% for early-fusion of features. Attempts were made to reduce feature space dimensionality by manually picking features as well as by use of the principal component analysis. The outcomes showed a significant decrease in accuracy. The obtained results have been visually compared with the ground truth. The observed compliance can be considered satisfactory.
Publisher
Slovenian Society for Stereology and Quantitative Image Analysis
Subject
Computer Vision and Pattern Recognition,Acoustics and Ultrasonics,Radiology, Nuclear Medicine and imaging,Instrumentation,Materials Science (miscellaneous),General Mathematics,Signal Processing,Biotechnology
Cited by
7 articles.
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