Computer Vision Based Monitoring System for Flotation in Mining Industry 4.0-Reference-Cited by-同舟云学术

Computer Vision Based Monitoring System for Flotation in Mining Industry 4.0

Published:2024 Issue: Volume: Page:152-163
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:en
Short-container-title:

Author:

Bendaouia Ahmed^ORCID,Abdelwahed El Hassan^ORCID,Qassimi Sara^ORCID,Boussetta Abdelmalek,Benzakour Intissar,Ahricha Mustapha,Amar Oumkeltoum,Bourzeix François

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-66705-3_10

Reference20 articles.

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2. Naseri, A., Rezaei Nasab, A.: Automatic identification of minerals in thin sections using image processing. J. Ambient. Intell. Humaniz. Comput. 14(4), 3369–3381 (2023). https://doi.org/10.1007/s12652-021-03474-5

3. Wang, Y.D., Shabaninejad, M., Armstrong, R.T., Mostaghimi, P.: Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images. Appl. Soft Comput. 104, 107185 (2021). https://doi.org/10.1016/j.asoc.2021.107185

4. Liu, Y., Zhang, Z., Liu, X., Wang, L., Xia, X.: Efficient image segmentation based on deep learning for mineral image classification. Adv. Powder Technol. 32(10), 3885–3903 (2021). https://doi.org/10.1016/j.apt.2021.08.038

5. Roslin, A., Marsh, M., Provencher, B., Mitchell, T.R., Onederra, I.A., Leonardi, C.R.: Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part II: semantic segmentation using a 2.5D CNN. Miner. Eng. 195, 108027 (2023). https://doi.org/10.1016/j.mineng.2023.108027