A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-023-02232-y.pdf
Reference56 articles.
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2. Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data,8(1), 53. https://doi.org/10.1186/s40537-021-00444-8
3. Arsomngern, P., Long, C., Suwajanakorn, S., & Nutanong, S. (2023). Towards pointsets representation learning via self-supervised learning and set augmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence,45(1), 1201–1216. https://doi.org/10.1109/TPAMI.2021.3139113
4. Baumann, F. W., Eichhoff, J. R., & Roller, D. (2017). Scanned image data from 3D-printed specimens using fused deposition modeling. Data. https://doi.org/10.3390/data2010003
5. Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding,110(3), 346–359. https://doi.org/10.1016/j.cviu.2007.09.014
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