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A Method for Analyzing the Composition of Petrographic Thin Section Image

  • Lanfang DongEmail author
  • Zhongya Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11901)

Abstract

The recognition and content analysis of the components in petrographic thin section image is a valuable study in geology. In this paper, we propose a two-stage method to segmentation and recognition of petrographic thin section image. In the first stage, we propose an image segmentation algorithm that can adaptively generate superpixel numbers based on SLIC algorithm. The algorithm is able to continually correct the number of superpixels in the iteration and then cluster the pixels of the image into superpixels by both color and spatial features. In the second stage, we designed a convolutional neural network and trained it with mineral grain images, which is then used to classify the superpixels obtained by first-stage. Finally, we count the categories and content of the components in the image based on the segmentation and classification results. We collected some images and invited geologists to label them for experimentation. The experimental results demonstrate the following: (1) Our proposed image segmentation algorithm is capable of dynamically generating the superpixels by the number of mineral grains in the image. (2) The CNN model we designed can accurately identify the categories of superpixel regions and has a small size. (3) The two-stage method is very effective in identifying the category of major components in an image and accurately estimating the content.

Keywords

Petrographic thin section image Superpixels Image segmentation Image recognition Component analysis 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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