Image Segmentation Algorithm Based on Improved U-Net for Mineral Froth Flotation Process
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7545-7_10
Reference13 articles.
1. Nayak A, Jena MS, Mandre NR (2022) Beneficiation of lead-zinc ores—a review. Miner Process Extr Metall Rev 43(5):564–583
2. Tan J, Liang L, Peng Y et al (2019) Effect of particle size on the froth property in coal flotation. J China Univ Min Technol 48(1):176–184
3. Galas J, Litwin D (2022) Machine learning technique for recognition of flotation froth images in a nonstable flotation process. Minerals 12(8):1052
4. Xie D, Lu M, Xie Y et al (2019) A fast threshold segmentation method for froth image base on the pixel distribution characteristic. PLoS ONE 14(1):e0210411
5. Peng C, Liu Y, Gui W et al (2021) Bubble image segmentation based on a novel watershed algorithm with an optimized mark and edge constraint. IEEE Trans Instrum Meas 71:1–10
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