References
- Asmussen, P., Conrad, O., Günther, A., Kirsch, M., & Riller, U. (2015). Semi-automatic segmentation of petrographic thin section images using a “seeded-region growing algorithm” with an application to characterize wheathered subarkose sandstone. Computers & Geosciences, 83, 89–99.
https://doi.org/10.1016/j.cageo.2015.05.001 - Chernov, V., Alander, J., & Bochko, V. (2015). Integer-based accurate conversion between RGB and HSV color spaces. Computers and Electrical Engineering, 46, 328–337.
https://doi.org/10.1016/j.compeleceng.2015.08.005 - Ermanovics, I. F. (1967). Statistical application of plagioclase extinction in provenance studies. Journal of Sedimentary Research, 37(2), 683–687.
https://doi.org/10.1306/74D7174E-2B21-11D7-8648000102C1865D - Latif, G., Bouchard, K., Maitre, J., Back, A., & Bédard, L. P. (2022). Deep-learning-based automatic mineral grain segmentation and recognition. Minerals, 12(4).
https://doi.org/10.3390/min12040455 - Li, Y., Onasch, C. M., & Guo, Y. (2008). GIS-based detection of grain boundaries. Journal of Structural Geology, 30(4), 431–443.
https://doi.org/10.1016/j.jsg.2007.12.007 - Naseri, A., & Rezaei Nasab, A. (2023). Automatic identification of minerals in thin sections using image processing. Journal of Ambient Intelligence and Humanized Computing, 14(4), 3369–3381.
https://doi.org/10.1007/s12652-021-03474-5 - Perera, R., Guzzetti, D., & Agrawal, V. (2021). Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images. Computational Materials Science, 196, 110524.
https://doi.org/10.1016/j.commatsci.2021.110524 - Reinhardt, J. (2004). Optical mineralogy in a modern earth sciences curriculum. Journal of Geoscience Education, 52(1), 60–67.
https://doi.org/10.5408/1089-9995-52.1.60 - Reinhardt, J., & Raith, M. M. Minerals in thin section (MINTS).
https://mints.uwc.ac.za/mints/home - Tarquini, S., & Favalli, M. (2010). A microscopic information system (MIS) for petrographic analysis. Computers and Geosciences, 36(5), 665–674.
https://doi.org/10.1016/j.cageo.2009.09.017 - Visalli, R., Ortolano, G., Godard, G., & Cirrincione, R. (2021). Micro-fabric analyzer (MFA): A new semiautomated ArcGIS-based edge detector for quantitative microstructural analysis of rock thin-sections. ISPRS International Journal of Geo-Information, 10(2).
https://doi.org/10.3390/ijgi10020051 - Yu, J., Wellmann, F., Virgo, S., von Domarus, M., Jiang, M., Schmatz, J., & Leibe, B. (2023). Superpixel segmentations for thin sections: Evaluation of methods to enable the generation of machine learning training data sets. Computers and Geosciences, 170, 105232.
https://doi.org/10.1016/j.cageo.2022.105232 - Zhang, Y., Zhong, H. R., Zhang, X., Gao, S. C., & Zhang, D. (2020). Orthogonal microscopy image acquisition analysis technique for rock sections in polarizer angle domain. Journal of Structural Geology, 140, 104174.
https://doi.org/10.1016/j.jsg.2020.104174 - Zhang, P., Zhou, J., Zhao, W., Li, X., & Pu, L. (2024). The edge segmentation of grains in thin-section petrographic images utilising extinction consistency perception network. Complex and Intelligent Systems, 10(1), 1231–1245.
https://doi.org/10.1007/s40747-023-01208-y