Lithological classification and chemical component estimation based on the visual features of crushed rock samples-Reference-Cited by-同舟云学术

Lithological classification and chemical component estimation based on the visual features of crushed rock samples

Published:2017-07-29 Issue:15 Volume:10 Page:
ISSN:1866-7511
Container-title:Arabian Journal of Geosciences
language:en
Short-container-title:Arab J Geosci

Author:

Khorram Farzaneh,Morshedy Amin Hossein,Memarian Hossein,Tokhmechi Behzad,Zadeh Hamid Soltanian

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

http://link.springer.com/article/10.1007/s12517-017-3116-8/fulltext.html

Reference32 articles.

1. ASTM International (2011) ASTM C-25 standard test methods for chemical analysis of limestone, quicklime, and hydrated lime. West Conshohocken, PA, 38 pp

2. Barker AJ (2014) A key for identification of rock-forming minerals in thin section. CRC Press, 171 pp

3. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, New York

4. Bonifazi G (1995) Digital multi-spectral techniques and automated image analysis procedures for industrial ore modeling. Miner Eng 8:779–794

5. Casali A, Gonzalez G, Vallebuona G, Perezq C, Vargas R (2001) Grind ability soft-sensors based on lithological composition and on-line measurements. Miner Eng 14:689–700

Cited by 13 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 review of deep leaning in image classification for mineral exploration;Minerals Engineering;2023-12

2. Deep learning based data augmentation for large-scale mineral image recognition and classification;Minerals Engineering;2023-12

3. Prediction and classification of minerals using deep residual neural network;Neural Computing and Applications;2023-11-09

4. MADENLERİN SINIFLANDIRILMASINA YÖNELİK HİBRİD BİR CNN MODELİN OLUŞTURULMASI;Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi;2023-09-03

5. LOSN: Lightweight ore sorting networks for edge device environment;Engineering Applications of Artificial Intelligence;2023-08