The Role of Classical Image Processing Algorithms in the Age of AI Revolution

Gądek-Moszczak, Aneta

doi:10.37705/TechTrans/e2024025

Abstract

The article is devoted to the analysis of AI methods used in materials science and, in particular, the possibility of using them in the automatic classification and detection of material structure elements in digital images. The main areas of application of AI algorithms in materials science, such as prediction of material properties, material design, and optimization of technological processes, are presented. Examples of machine learning algorithm applications for classifying structure elements are discussed. Difficulties encountered in implementing AI algorithms in analyzing images of material microstructures were analyzed.

References

Akinpelu A., Bhullar M., Yao Y. (2024). Discovery of novel materials through machine learning. Journal of Physics Condensed Matter 36(45), art. 453001. https://doi.org/10.1088/1361-648X/ad6bdb
Search in Google Scholar Back to article
Almukhalfi, H., Noor, A., Noor, T.H. (2024). Traffic management approaches using machine learning and deep learning techniques: A survey. Engineering Applications of Artificial Intelligence 133, Part B, https://doi.org/10.1016/j.engappai.2024.108147.
Search in Google Scholar Back to article
Brian L. DeCost, Elizabeth A. Holm, A. (2015). Computer vision approach for automated analysis and classification of microstructural image data. Comput. Mater. Sci. 110, 126–133. https://doi.org/10.1016/j.commatsci.2015.08.011
Search in Google Scholar Back to article
Choudhary, K., DeCost, B., Chen, C., Jain A., Tavazza, F., Cohn, R., Park, C.W., Choudhary, A., Agrawal, A., Billinge, S.J.L., Holm, E., Ong, S.P., Wolverton, C. (2022). Recent advances and applications of deep learning methods in materials science. npj Computational Materials 8(1), art. 59. https://doi.org/10.48550/arXiv.2110.14820
Search in Google Scholar Back to article
Cruz-Orive, L.M., (2024). Stereology. Theory and Application, Springer, Interdisciplinary Applied Mathematics.
Search in Google Scholar Back to article
Diwakar V., Sharma A., Yusufzai, M.Z.K., Vashista, M. (2024). Machine learning--based prediction of single clad characteristics and non-destructive characterization of multi-layer deposited FeCoNiCrMo HEA on EN24 via laser cladding. Materials Today Communications 41, art. 110839. https://doi.org/10.1016/j.mtcomm.2024.110839
Search in Google Scholar Back to article
Ghilom, M., Latifi, S. (2024). The Role of Machine Learning in Advanced Biometric Systems. Electronics 13(13), art. 2667. https://doi.org/10.3390/electronics13132667
Search in Google Scholar Back to article
Gururaj, H.L., Soundarya, B.C., Priya, S., Shreyas, J., Flammini, F. (Accepted/In press). A Comprehensive Review of Face Recognition Techniques, Trends and Challenges. IEEE Access 12, art. 1. https://doi.org/10.1109/ACCESS.2024.3424933
Search in Google Scholar Back to article
Hendrix, W., Hendrix, N., Scholten, E.T. et al. (2023). Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans. Commun Med 3, 156. https://doi.org/10.1038/s43856-023-00388-5
Search in Google Scholar Back to article
Islam, M.D, Rashid, S.I, Hossain, N.U.I, Fleming, R., Sokolov A., (2023). An integrated convolutional neural network and sorting algorithm for image classification for efficient flood disaster management. Decision Analytics Journal 7, art. 100225. https://doi.org/10.1016/j.dajour.2023.100225
Search in Google Scholar Back to article
Jain, S., Singh, A., Shah, S.N., Lalam, R., Saxena, D. (2021). Machine Learning-Based Real-Time Traffic Control System, 2021 IEEE Mysore Sub Section International Conference (MysuruCon), Hassan, India, 92–97. https://doi.org/10.1109/MysuruCon52639.2021.9641643.
Search in Google Scholar Back to article
Jaspin, K., Yogasundar, K., Shree Vishnu, S. (2024). Satellite Images Classification by Using Artificial Intelligence Techniques, 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET), Nagpur, India, 1–6. https://doi.org/10.1109/ICICET59348.2024.10616283.
Search in Google Scholar Back to article
DeMille, K.J., Hall, R., Leigh, J.R., Guven, I., Spear, A.D. (2024). Materials design using genetic algorithms informed by convolutional neural networks: Application to carbon nanotube bundles. Composites Part B: Engineering 286, art. 111751. https://doi.org/10.1016/j.compositesb.2024.111751
Search in Google Scholar Back to article
Koh, D.-M., Papanikolaou, N., Bick, U., Illing, R., Kahn, C.E. Jr., Kalpathi-Cramer, J., Matos, C., Martí-Bonmatí, L., Miles, A., Mun, S.K., Napel, S., Rockall, A., Sala, E., Strickland, N., Prior, F. (2022). Artificial intelligence and machine learning in cancer imaging. Commun Med. 2, 133. https://doi.org/10.1038/s43856-022-00199-0
Search in Google Scholar Back to article
Morgan, D., Jacobs, R. (2020). Opportunities and Challenges for Machine Learning in Materials Science. Annual Review of Materials Research 50, 71–103. https://doi.org/10.1146/annurev-matsci-070218-010015
Search in Google Scholar Back to article
Müller, M., Britz, D., Staudt, T., Mücklich, F. (2021). Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques. Metals 11(11), art. 1836. https://doi.org/10.3390/met11111836
Search in Google Scholar Back to article
Nakrani, M.G., Sable, G.S., Shinde, U.B. (2020). ResNet based Lung Nodules Detection from Computed Tomography Images. International Journal of Innovative Technology and Exploring Engineering 9(4), 1711–1714. https://doi.org/10.35940/ijitee.D1540.029420
Search in Google Scholar Back to article
Pal, O.K., Roy, S., Modok, A.K., Teethi, T.I., Sarker, S.K. (2024). ULung: A Novel Approach for Lung Image Segmentation. 6th International Conference on Computing and Informatics (ICCI), New Cairo–Cairo, Egypt, 2024, 522–527. https://doi.org/10.1109/ICCI61671.2024.10485043
Search in Google Scholar Back to article
Sowmya, D., Deepa, P., Venugopal, K. (2017). Remote sensing satellite image processing techniques for image classification: a comprehensive survey. Int. J. Comput. Appl. 161(11), 24–37. https://doi.org/10.5120/ijca2017913306
Search in Google Scholar Back to article
Tehrani, A.M., Oliynyk, A.O., Parry, M., Rizvi, Z., Couper S., Lin F., Miyagi L., Sparks T.D., Brgoch J. (2018). Machine learning directed search for ultraincompressible, superhard materials. J. Am. Chem. Soc. 140, 9844–9853. https://doi.org/10.1021/jacs.8b02717
Search in Google Scholar Back to article
Tian, J., Zhao, Y., Huang, Y., Li,Y., Zhang, Ch., Peng, S., Han, G., Liu,Y., (2024). Theoretical Prediction of Vickers Hardness for Oxide Glasses: Machine Learning Model, Interpretability Analysis, and Experimental Validation. Materialia 33, art. 102006. https://doi.org/10.1016/j.mtla.2024.102006
Search in Google Scholar Back to article
Toorandaz, S., Taherkhani, K., Liravi, F., Toyserkani, E. (2024). A novel machine learning-based approach for in-situ surface roughness prediction in laser powder-bed fusion. Additive Manufacturing 91, art. 104354. https://doi.org/10.1016/j.addma.2024.104354
Search in Google Scholar Back to article
Tran, K., Neiswanger, W., Yoon, J., Zhang, Q., Xing, E., Ulissi, Z.W. (2020). Methods for comparing uncertainty quantifications for material property predictions. Machine Learning: Science and Technology 1(2), art. 025006. https://doi.org/10.48550/arXiv.1912.10066
Search in Google Scholar Back to article
Wang, Z.-L., Adachi, Y. (2019). Property prediction and properties-tomicrostructure inverse analysis of steels by a machine-learning approach. Materials Science and Engineering A, 744, 661–670. https://doi.org/10.1016/j.msea.2018.12.049
Search in Google Scholar Back to article
Wang Z., Chang S., Bao X., Yu H., Guan S., Zhu K., Zheng Y., Li J., Gao X. (2024). Interpretable prediction of remanence in sintered NdFeB through machine learning strategy. Journal of Alloys and Compounds 1008, art. 176727. https://doi.org/10.1016/j.jallcom.2024.176727
Search in Google Scholar Back to article
Villars, P., Cenzua, l.K. (2018). Pearson’s crystal data: crystal structure database for inorganic compounds. ASM International, Materials Park, 2010.
Search in Google Scholar Back to article
Xie, T., Grossman, J.C. (2018). Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Physical Review Letters 120(14), art. 145301. https://doi.org/10.1103/PhysRevLett.120.145301
Search in Google Scholar Back to article
Zhang, J., Marszałek, M., Lazebnik, S., Schmid, C. (2007). Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision 73(2), 213–238. https://doi.org/10.1007/s11263-006-9794-4
Search in Google Scholar Back to article
Zhao, Z. (2024). Predicting compressive strength of ultra-high-performance concrete using Naive Bayes regression in novel approaches. Multiscale and Multidisciplinary Modeling, Experiments and Design 7(6), 5235–5249. https://doi.org/10.1007/s41939-024-00511-6.
Search in Google Scholar Back to article
Zhao, P., Wang, Y., Jiang, B., Wei, M., Zhang, H., Cheng, X., (2023). A new method for classifying and segmenting material microstructure based on machine learning. Materials & Design 227, art. 111775. https://doi.org/10.1016/j.matdes.2023.111775
Search in Google Scholar Back to article
Zhu, B., Chen, Z., Hu, F. et al. (2022). Feature Extraction and Microstructural Classification of Hot Stamping Ultra-High Strength Steel by Machine Learning. JOM 74, 3466–3477. https://doi.org/10.1007/s11837-022-05265-5
Search in Google Scholar Back to article
Zimmermann, M.G., Lengler, J., Glushych, V., Schopphoven, T., Meiners, W., Häfner, C.L., Holly, C. (2024). AI-based spatially resolved parameter prediction in laser metal deposition for increased process stability. Journal of Laser Applications 36(4), art. 042075. https://doi.org/10.2351/7.0001604
Search in Google Scholar Back to article

The Role of Classical Image Processing Algorithms in the Age of AI Revolution

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