期刊
IMAGE ANALYSIS & STEREOLOGY
卷 39, 期 2, 页码 73-90出版社
INT SOC STEREOLOGY
DOI: 10.5566/ias.2186
关键词
classification; grain sizes; object segmentation; texture features
类别
资金
- Quality Grant of the President of Silesian University of Technology [02/020/RGJ19/0168]
- SUT grant
- Central Mining Institute [GIG: 11010117144]
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 this 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 75% accuracy. The multi-texture approach was also investigated, leading to an increase in accuracy to over 79% for the early-fusion of features. Attempts were made to reduce feature space dimensionality by manually picking features as well as by the use of principal component analysis. The outcomes showed a significant decrease in accuracy. The obtained results have been visually compared with the ground truth. The compliance observed can be considered to be satisfactory.
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