Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning
Author:
Funder
Ministry of Science and Technology, Taiwan
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02474-4.pdf
Reference36 articles.
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2. Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C. M., & Dario, P. (2020). Visual-based defect detection and classification approaches for industrial applications—a survey. Sensors. https://doi.org/10.3390/s20051459
3. Galloway, M. M. (1975). Texture analysis using gray level run lengths. Computer Graphics and Image Processing, 4(2), 172–179. https://doi.org/10.1016/S0146-664X(75)80008-6
4. Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/TSMC.1973.4309314
5. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR.2016.90.
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