Classification of rocks is one of the fundamental tasks in a geological study. The process requires a human expert to examine sampled thin section images under a microscope. In this study, we propose a method that uses microscope automation, digital image acquisition, edge detection and colour analysis (histogram). We collected 60 digital images from 20 standard thin sections using a digital camera mounted on a conventional microscope. Each image is partitioned into a finite number of cells that form a grid structure. Edge and colour profile of pixels inside each cell determine its classification. The individual cells then determine the thin section image classification via a majority voting scheme. Our method yielded successful results as high as 90% to 100% precision.
@article{arxiv.1710.00189,
title = {Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis},
author = {S. Joseph and H. Ujir and I. Hipiny},
journal= {arXiv preprint arXiv:1710.00189},
year = {2021}
}
Comments
Published in 2017 IEEE International Conference On Signal and Image Processing Applications