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Abstract-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.

Decorative plaited mat is one of the many examples of rich plait work often seen on Borneo handicraft products. The plaited mats are decorated with simple and complex motif designs; each has its own special meaning and taboos. The motif designs are used as a reflection of environment and the traditional beliefs in the Iban community. In line with efforts from UNESCO’s and Sarawak Government’s, digitization, and the use of IR4.0 technologies to preserve and promote this cultural heritage is encouraged. Towards this end goal, we present a novel image dataset containing 10 Iban plaited mat motif classes. The plaited mat motifs are made of diagonal and symmetrical shapes, as well as geometric and non-geometric patterns. Classification’s accuracy using scale-invariant feature transform (SIFT) features was evaluated against 6 common image deformations: zoom+rotation, viewpoint, image blur, JPEG compression, scale and illumination, across multiple threshold values. Varying degrees of each deformation were applied to a digitally cleaned (and cropped) image of each mat motif class. We used RANSAC to remove outliers from the noisy SIFT matching result. The optimal threshold value is 2.0e-2 with a reported 100.0% matching accuracy for the scale change and zoom+rotation set.

<span lang="EN-US">Decorative mats plaited by the Iban communities in Borneo contains motifs that reflect their traditional beliefs. Each motif has its own special meaning and taboos. A typical mat motif contains multiple smaller patterns that surround the main motif hence is likely to cause misclassification. We introduce a classification framework with adaptive sampling to remove smaller features whilst retaining larger (and discriminative) image structures. Canny filter and probabilistic hough transform are gradually applied to a clean greyscale image until a threshold value pertaining to the image’s structural information is reached. Morphological dilation is then applied to improve the appearance of the retained edges. The resulting image is described using binary robust invariant scalable keypoints (BRISK) features with random sample consensus (RANSAC). We reported the classification accuracy against six common image deformations at incremental degrees: scale+rotation, viewpoint, image blur, joint photographic experts group (JPEG) compression, scale and illumination. From our sensitivity analysis, we found the optimal threshold for adaptive smoothing to be 75.0%. The optimal scheme obtained 100.0% accuracy for JPEG compression, illumination, and viewpoint set. Using adaptive smoothing, we achieved an average increase in accuracy of 11.0% compared to the baseline.</span>

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