Automatic grain segmentation in cross-polarized photomicrographs of sedimentary rocks using psychophysics inspired models
Author:
Funder
Science and Engineering Research Board
Indian Statistical Institute
Publisher
Springer Science and Business Media LLC
Subject
Software
Link
https://link.springer.com/content/pdf/10.1007/s11334-021-00400-y.pdf
Reference36 articles.
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2. Mokhles M, Fatai A, Mohammed M (2019) Advances in rock petrography: image processing techniques for automated textural thin section analysis. In: Proceedings of SPE middle east oil and gas show and conference, Manama, Bahrain, March 2019. ISBN: 978–1–61399–639–3
3. Maitre J, Kévin Bouchard L, Bédard P (2019) Mineral grains recognition using computer vision and machine learning. Comput Geosci 130:84–93
4. Budennyy S, Pachezhertsev A, Bukharev A, Erofeev A, Mitrushkin D, Belozerov B (2017) Image processing and machine learning approaches for petrographic thin section analysis. In: Proceedings of SPE Russian petroleum technology conference, Moscow, Russia, October 2017. ISBN: 978–1–61399–587–7
5. Heilbronner R (2000) Automatic grain boundary detection and grain size analysis using polarization micrographs or orientation images. J Struct Geol 22(7):969–981
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A framework for microscopic grains segmentation and Classification for Minerals Recognition using hybrid features;Earth Science Informatics;2024-09-12
2. The edge segmentation of grains in thin-section petrographic images utilising extinction consistency perception network;Complex & Intelligent Systems;2023-08-26
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