Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: atom-ph