Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis
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 anal...
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| Main Authors: | , , |
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| Format: | Proceeding |
| Language: | en |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/39747/1/Unsupervised%20Classification%20of%20Intrusive%20Igneous.pdf http://ir.unimas.my/id/eprint/39747/ https://ieeexplore.ieee.org/document/8120669 |
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| Summary: | 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. |
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