Труды КНЦ (Технические науки вып.3/2025(16))

Список источников 1. Weibel E. R., Elias H. Introduction to stereologic principles // Quantitative Methods in Morphology / Quantitative Methoden in der Morphologie / eds. E. R. Weibel, H. Elias. Berlin, Heidelberg: Springer, 1967. P. 89-98. 2. Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks / A. Chattopadhay [и др.] // 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). 2018. Grad-CAM++. С. 839-847. 3. Srinivas S., Fleuret F. Full-Gradient Representation for Neural Network Visualization // Advances in Neural Information Processing Systems. Curran Associates, Inc., 2019. Vol. 32. 4. Baklanova O., Shvets O. Cluster analysis methods for recognition of mineral rocks in the mining industry // 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA) 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA). 2014. P. 1-5. 5. Baklanova O. E., Baklanov M. A. Methods and Algorithms of Image Recognition forMineral Rocks in the Mining Industry // Advances in Swarm Intelligence: Lecture Notes in Computer Science / eds. Y. Tan, Y. Shi, L. Li. Cham: Springer International Publishing, 2016. P. 253-262. 6. Launeau P., Cruden A., Bouchez J. Mineral recognition in digital images of rocks: a new approach using multichannel classification // CanadianMineralogist. 1994. Mineral recognition in digital images of rocks. No. 32. P. 919-933. 7. Review of Nodule Mineral Image Segmentation Algorithms for Deep-Sea Mineral Resource Assessment / W. Song [et al.] // Computers, Materials and Continua. 2022. Vol. 73. No. 1. P. 1649-1669. 8. Deep learning-based method for SEM image segmentation in mineral characterization, an example fromDuvernay Shale samples in Western Canada Sedimentary Basin / Z. Chen [et al.] // Computers & Geosciences. 2020. Vol. 138. P. 104450. 9. Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro- CT images / Y. D. Wang [et al.] // Applied Soft Computing. 2021. Vol. 104. P. 107185. 10. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects / Z. Li [et al.] // IEEE Transactions on Neural Networks and Learning Systems. 2022. Vol. 33. A Survey of Convolutional Neural Networks. No. 12. P. 6999-7019. 11. Deep Residual Learning for Image Recognition / K. He [et al.] // 2016 IEEE Conference on Computer Vision and PatternRecognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016. P. 770-778. 12. MineralImage5k: A benchmark for zero-shot raw mineral visual recognition and description / S. Nesteruk [et al.] // Computers & Geosciences. 2023. Vol. 178. MineralImage5k. P. 105414. 13. Yen S.-J., Lee Y.-S. Under-Sampling Approaches for Improving Prediction of the Minority Class in an Imbalanced Dataset // Intelligent Control and Automation: International Conference on Intelligent Computing, ICIC 2006 Kunming, China, August 16-19, 2006: Lecture Notes in Control and Information Sciences / eds. D.-S. Huang, K. Li, G. W. Irwin. Berlin, Heidelberg: Springer, 2006. P. 731-74. References 1. Weibel E. R., Elias H. Introduction to stereologic principles. Quantitative Methods in Morphology / Quantitative Methoden in der Morphologie / eds. E. R. Weibel, H. Elias. Berlin, Heidelberg, Springer, 1967, pp. 89-98. 2. Chattopadhay A. et al. Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks. 2018 IEEE Winter Conference on Applications o f Computer Vision (WACV) 2018 IEEE Winter Conference on Applications o f Computer Vision (WACV), 2018, Grad-CAM++, pp. 839-847. 3. Srinivas S., Fleuret F. Full-Gradient Representation for Neural Network Visualization. Advances in Neural Information Processing Systems. Curran Associates, Inc., 2019, vol. 32. 4. Baklanova O., Shvets O. Cluster analysis methods for recognition of mineral rocks in the mining industry. 2014 4th International Conference on Image Processing Theory, Tools andApplications (IPTA) 2014 4th International Conference on Image Processing Theory, Tools andApplications (IPTA), 2014, pp. 1-5. 5. Baklanova O. E., Baklanov M. A. Methods and Algorithms of Image Recognition forMineral Rocks in the Mining Industry. Advances in Swarm Intelligence: Lecture Notes in Computer Science / eds. Y. Tan, Y. Shi, L. Li. Cham, Springer International Publishing, 2016, pp. 253-262. 6. Launeau P., Cruden A., Bouchez J. Mineral recognition in digital images of rocks: a new approach using multichannel classification. Canadian Mineralogist, 1994, Mineral recognition in digital images of rocks, no. 32, pp. 919-933. Труды Кольского научного центра РАН. Серия: Технические науки. 2025. Т. 16, № 3. С. 106-116. Transactions of the Kola Science Centre of RAS. Series: Engineering Sciences. 2025. Vol. 16, No. 3. P. 106-116. © Диковицкий В. В., Шишаев М. Г., 2025 115