參考文獻 |
1. Bahrin, M. A. K., Othman, M. F., Azli, N. H. N., & Talib, M. F., “Industry 4.0: A review on industrial automation and robotic.”, Jurnal Teknologi, vol. 78(6-13), 2016.
2. 張頌榮,「TFT-LCD 面板之點線瑕疵自動化檢測系統」,國立成功大學製造工程研究所碩博士班碩士論文,2005。
3. 李柏蒼,「TFT-LCD高階光學檢測設備國產化策略」,國立清華大學高階經營管理碩士在職專班碩士論文。2009。
4. Li, Z., & Yang, Q., “System design for PCB defects detection based on AOI technology.”, In 2011 4th International Congress on Image and Signal Processing, vol. 4, pp. 1988-1991, IEEE, 2011
5. Dai, W., Mujeeb, A., Erdt, M., & Sourin, A., “Soldering defect detection in automatic optical inspection.”, Advanced Engineering Informatics, vol. 43, 2020.
6. Luo, Q., Fang, X., Liu, L., Yang, C., & Sun, Y., “Automated visual defect detection for flat steel surface: A survey.”, IEEE Transactions on Instrumentation and Measurement, vol. 69(3), pp. 626-644, 2020.
7. Wei, X., Jiang, S., Li, Y., Li, C., Jia, L., & Li, Y., “Defect detection of pantograph slide based on deep learning and image processing technology.”, IEEE Transactions on Intelligent Transportation Systems, vol. 21(3), pp. 947-958, 2019.
8. Cha, Y. J., Choi, W., & Büyüköztürk, O., “Deep learning‐based crack damage detection using convolutional neural networks.”, Computer‐Aided Civil and Infrastructure Engineering, vol. 32(5), pp. 361-378, 2017.
9. Baur, C., Wiestler, B., Albarqouni, S., & Navab, N., “Deep autoencoding models for unsupervised anomaly segmentation in brain MR images.”, In International MICCAI Brainlesion Workshop, pp. 161-169, Springer, 2018.
10. Davletshina, D., Melnychuk, V., Tran, V., Singla, H., Berrendorf, M., Faerman, E., Fromm, M., & Schubert, M., “Unsupervised anomaly detection for x-ray images.”, arXiv preprint arXiv:2001.10883, 2020.
11. Saligrama, V., & Chen, Z., “Video anomaly detection based on local statistical aggregates.”, In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2112-2119, IEEE, 2012.
12. Zhou, J. T., Du, J., Zhu, H., Peng, X., Liu, Y., & Goh, R. S. M., “AnomalyNet: An anomaly detection network for video surveillance.”, IEEE Transactions on Information Forensics and Security, vol. 14(10), pp. 2537-2550, 2019.
13. Valdes, A., & Cheung, S., “Communication pattern anomaly detection in process control systems.”, In 2009 IEEE Conference on Technologies for Homeland Security, pp. 22-29, IEEE, 2009.
14. Ten, C. W., Hong, J., & Liu, C. C., “Anomaly detection for cybersecurity of the substations.”, IEEE Transactions on Smart Grid, vol. 2(4), pp. 865-873, 2011.
15. Gaus, Y. F. A., Bhowmik, N., Akçay, S., Guillén-Garcia, P. M., Barker, J. W., & Breckon, T. P., “Evaluation of a dual convolutional neural network architecture for object-wise anomaly detection in cluttered X-ray security imagery.”, In 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, IEEE, 2019.
16. Akçay, S., Atapour-Abarghouei, A., & Breckon, T. P., “Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection.”, In 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, IEEE, 2019.
17. Chandola, V., Banerjee, A., & Kumar, V., “Anomaly detection: A survey.”, ACM computing surveys (CSUR), vol. 41(3), pp. 1-58, 2009.
18. Sakurada, M., & Yairi, T., “Anomaly detection using autoencoders with nonlinear dimensionality reduction.”, In Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, pp. 4-11, 2014
19. Chen, Z., Yeo, C. K., Lee, B. S., & Lau, C. T., “Autoencoder-based network anomaly detection.”, In 2018 Wireless Telecommunications Symposium (WTS), pp. 1-5, IEEE.
20. Chow, J. K., Su, Z., Wu, J., Tan, P. S., Mao, X., & Wang, Y. H., “Anomaly detection of defects on concrete structures with the convolutional autoencoder.”, Advanced Engineering Informatics, vol. 45, 2020.
21. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., & Bengio, Y., “Generative adversarial networks.”, arXiv preprint arXiv:1406.2661, 2014.
22. Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G., “Unsupervised anomaly detection with generative adversarial networks to guide marker discovery.”, In International conference on information processing in medical imaging, pp. 146-157, Springer, Cham, 2017.
23. Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P., “Ganomaly: Semi-supervised anomaly detection via adversarial training.”, In Asian conference on computer vision, pp. 622-637, Springer, Cham, 2018.
24. Hinton, G. E., & Salakhutdinov, R. R., “Reducing the dimensionality of data with neural networks.”, science, vol. 313(5786), no. 504-507, 2006.
25. Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P. A., “Extracting and composing robust features with denoising autoencoders.”, In Proceedings of the 25th international conference on Machine learning, pp. 1096-1103, 2008.
26. Lee, H., Battle, A., Raina, R., & Ng, A. Y., “Efficient sparse coding algorithms.”, In Advances in neural information processing systems, pp. 801-808, 2007.
27. Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B., “Adversarial autoencoders.”, arXiv preprint arXiv:1511.05644, 2015.
28. Radford, A., Metz, L., & Chintala, S., “Unsupervised representation learning with deep convolutional generative adversarial networks.”, arXiv preprint arXiv:1511.06434, 2015.
29. Gowda, S. N., & Yuan, C., “ColorNet: Investigating the importance of color spaces for image classification.”, In Asian Conference on Computer Vision, pp. 581-596, Springer, Cham, 2018.
30. Chaudhary, P., Chaudhari, A. K., Cheeran, A. N., & Godara, S., “Color transform based approach for disease spot detection on plant leaf.”, International journal of computer science and telecommunications, vol. 3(6), pp. 65-70, 2012.
31. Schanda, J., Colorimetry: understanding the CIE system. John Wiley & Sons, 2007.
32. Hill, B., Roger, T., & Vorhagen, F. W., “Comparative analysis of the quantization of color spaces on the basis of the CIELAB color-difference formula.”, ACM Transactions on Graphics (TOG), vol. 16(2), pp. 109-154, 1997. |