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 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.Comment: To appear in 2017 IEEE International Conference On Signal and Image Processing Application

FCSIT Research Bulletin 2017

The FCSIT Research Bulletin is an annual publication of the Faculty of Computer Science and Information Technology, UNIMAS. The purpose of FCSIT Research Bulletin is to disseminate information that represent the current state of the research activities, publications, research findings, training, conferences and seminar conducted by the academicians in the faculty

FCSIT Research Bulletin 2016

The FCSIT Research Bulletin is an annual publication of the Faculty of Computer Science and Information Technology, UNIMAS. The purpose of FCSIT Research Bulletin is to disseminate information that represent the current state of the research activities, publications, research findings, training, conferences and seminar conducted by the academicians in the faculty