Параметрическая оптимизация идентификации при распознавании изображений пыльцевых зерен
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Основное содержимое статьи
Аннотация
Разработаны методы оптимизации идентификации пыльцевых зерен с использованием статистических, динамических, текстурных и специфических характеристик изображений. Исследованы точечная и нелинейная проверки соответствия контуров вводимого и эталонного объектов – изображений пыльцевых зерен и регулирования растровых параметров. Реализованы механизмы редукции нулевых точек контура, уменьшения размерности растров, масштабирования, порогового и уровневого контроля, кодировании и размещения изображений микрообъектов на основе пирамидальной модели, отбора опорных точек контура, когнитивного анализа, поиска точек с отжигом, запретом, на основе стохастического моделирования по усеченной цепи Маркова. Реализован компьютерный комплекс идентификации на языке С++ в среде параллельных вычислений «CUDA» для распознавания, классификации и си-стематизации пыльцевых зерен.
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Библиографические ссылки
Zhang, Y., Liu, J., Wang, L. (2022). Deep learning-based pollen grain recognition using hybrid feature extraction. Pattern Recognition Letters, 160, 112–119. https://doi.org/10.1016/j.patrec.2022.06.008
Chen, Y., Xu, R., Wang, X. (2021). Texture and shape-based classification of microscopic pollen images using CNNs. In: Proceedings of the 2021 IEEE International Conference on Image Pro-cessing (ICIP), IEEE, 2045–2049. https://doi.org/10.1109/ICIP2021.9506543.
Li, H., Sun, Z., Zhao, J. (2020). Contour matching and dimensionality reduction in biological micro-object classification. In: Lecture Notes in Computer Science, vol 12355. Springer, Cham. https://doi.org/10.1007/978-3-030-58601-2_10.
Kumar, V., Sharma, N. (2022). Identification of microscopic structures using statistical image fea-tures and neural networks. Artificial Intelligence in Medicine, 126, 102234. https://doi.org/10.1016/j.artmed.2022.102234.
Nguyen, T.H., Le, Q.H. (2020). Pollen grain classification using pyramid histogram of oriented gradi-ents and SVM. Biosystems Engineering, 197, 31–40. https://doi.org/10.1016/j.biosystemseng.2020.06.005.
Wang, M., Liu, X., Zhou, F. (2021). Adaptive thresholding and edge reduction techniques for pollen contour analysis. In: Proceedings of the 2021 International Conference on Computer Vision (ICCV), IEEE, 1590–1598. https://doi.org/10.1109/ICCV48922.2021.00160.
Jumanov, I.I., Safarov, R.A. (2023). Optimization of Identification and Recognition of Micro-objects Based on the Use of Specific Image Characteristics. In: Hu, Z., Zhang, Q., He, M. (eds) Advances in Artificial Systems for Logistics Engineering III. ICAILE 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 180. Springer, Cham. https://doi.org/10.1007/978-3-031-36115-9_44.
Djumanov O.I., Kholmonov S.M. The modified model of training of neural networks in computer industrial systems with modules for nonstationary objects images processing // Journal of Korea Multimedia Society, Сhemical Technology. Control and Management. South Korea, Seoul – Uz-bekistan, Tashkent – 2016, № 5. p.54-58.
Jumanov, I.I., Xolmonov, S.M., Diumanov, I. Identification of non-Stationary Objects Based on Mul-ti-Parameter Optimization Tools // RusAutoCon - Proceedings of the International Russian Auto-mation Conference, 2024, (2024), страницы 1146–1152, DOI: 10.1109/RusAutoCon61949.2024.10694145.
Zhuk A. P., Stepanyan N. E., Kholmonov S. M. Stochastic Orthogonal Code Generator for a Perspec-tive Cognitive Radio System //International Workshop on Advanced Information Security Man-agement and Applications. – Cham : Springer Nature Switzerland, 2024. – С. 328-336.
Jumanov I. I., Kholmonov S.M., Djumanov O.I.& Dagur Arvind. Optimization of the identification of non-stationary objects under the influence of the internal and external environment //Artificial In-telligence and Information Technologies. – CRC Press, 2025. – с. 274-281.
Jumanov I. I., Kholmonov S.M., Djumanov O.I.& Dagur Arvind. Optimization of identification of non-stationary objects due to information reliability control mechanisms //Artificial Intelligence and Information Technologies. – CRC Press, 2024. – с. 282-288.
Petrou, M., Sevilla, P. (2018). Image Processing: Dealing with Texture and Shape Features. Springer, Cham. https://doi.org/10.1007/978-3-319-96821-4.
Oliveira, M., Falcão, A.X. (2019). Image pyramid-based representations for microstructure recogni-tion. Journal of Microscopy, 276(3), 194–205. https://doi.org/10.1111/jmi.12856.
Guo, R., Zhang, S. (2020). Markov Chain-based stochastic modeling in pollen image recognition. Mathematics and Computers in Simulation, 177, 15–27. https://doi.org/10.1016/j.matcom.2020.05.003.
Lee, J., Kim, Y. (2023). Feature selection for micro-image classification using cognitive analysis methods. Cognitive Systems Research, 81, 45–55. https://doi.org/10.1016/j.cogsys.2023.01.004.
Alzu’bi, A., Amira, A., Ramzan, N. (2017). Semantic image segmentation of pollen grains using hy-brid CNN approaches. Neurocomputing, 266, 575–588. https://doi.org/10.1016/j.neucom.2017.06.030.
Rana, A., Singh, M. (2021). Pollen recognition through contour reduction and support point detec-tion. Pattern Recognition, 112, 107741. https://doi.org/10.1016/j.patcog.2021.107741.
Xue, Y., Zhang, W. (2022). Dynamic regulation of raster parameters in micro-image processing. In: Lecture Notes in Electrical Engineering, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-030-95379-1_21.
Huang, C., Wang, T. (2021). GPU-accelerated classification of biological particles using CUDA. Journal of Parallel and Distributed Computing, 151, 123–131. https://doi.org/10.1016/j.jpdc.2021.01.008
Ahmad, M., Ali, F. (2023). Comparative analysis of threshold and level control for image segmenta-tion. Multimedia Tools and Applications, 82, 31947–31963. https://doi.org/10.1007/s11042-023-14725-7.
Stojanovic, J., Milinkovic, D. (2022). Heuristic optimization in image-based classification using simulated annealing. Applied Soft Computing, 122, 108814. https://doi.org/10.1016/j.asoc.2022.108814
Qureshi, M.A., Shoaib, M. (2020). Dimensionality reduction techniques for microscopic image data. Computer Methods and Programs in Biomedicine, 197, 105684. https://doi.org/10.1016/j.cmpb.2020.105684.
Morales, A., Guerra, S. (2021). Specific feature coding in biological image datasets. In: Communi-cations in Computer and Information Science, vol 1457. Springer, Cham. https://doi.org/10.1007/978-3-030-76819-7_13.