|
[1] Wang, W.-C., Chen, L.-B., Chang, W.-J., Chen, S.-L., & Li, S.-M. (2016). A Machine Vi-sion Based Automatic Optical Inspection System for Measuring Drilling Quality of Printed Cir-cuit Boards. IEEE Access, 99, 2169-3536. [2] Tsai, H.-H., Lai, Y.-S., & Lo, S.-C. (2013). A zero-watermark scheme with geometrical in-variants using SVM and PSO against geometrical attacks for image protection. Journal of Sys-tems and Software, 86(2), 335-348. [3] Tsai, H.-H., Chang, B.-M., & Liou, S.-H. (2014). Rotation- invariant texture image retrieval using particle swarm optimization and support vector regression. Applied Soft Computing, 17, 127-139. [4] Chang, B.-M., Tsai, H.-H., & Yen, C.-Y. (2016). SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains. Engineering Applications of Artificial Intelli-gence, 52, 96-107. [5] Tsai, H.-H., & Chang, Y.-C. (2017). Facial expression recognition using a combination of multiple facial features and support vector machine. Soft Computing, 22(13), 4389-4405. [6] Jung, A., imgaug, https:/github.com/aleju [7] Song, K.-C., & Yan, Y.-H. (2013). A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Applied Surface Science, 285, 858-864. [8] Chu, M.-X., Wang, A.-N., Gong, R.-F., & Sha, M. (2014). Multi-class Classification Meth-ods of Enhanced LS-TWSVM for Strip Steel Surface Defects. Journal Of Iron And Steel Re-search International, 21(2), 174-180. [9] Xu, K., Liu, S., & Ai, Y. (2015). Application of Shearlet transform to classification of sur-face defects for metals. Image And Vision Computing, 35, 23-30. [10] Shanmugamani, R., Sadique, M., & Ramamoorthy, B. (2015). Detection and classification of surface defects of gun barrels using computer vision and machine learning. Measurement, 60, 222-230. [11] Ahmed, S., Rao, B., & Jayakumar, T. (2016). Greedy breadth-first-search based chain clas-sification for nondestructive sizing of subsurface defects. Applied Soft Computing, 40, 260-273. [12] Hu, H.-J., Liu, Y., Liu, M.-F., & Nie, L.-Q. (2016). Surface defect classification in large-scale strip steel image collection via hybrid chromosome genetic algorithm. Neurocomputing, 181, 86-95. [13] Tao, X., Xu, D., Zhang, Z.-Y., Zhang, F., Liu, X.-L., & Zhang, D.-P. (2017). Weak scratch detection and defect classification methods for a large-aperture optical element. Optics Commu-nications, 387, 390-400. [14] Hanzaei, S., Afshar, A., & Barazandeh, F. (2017). Automatic detection and classification of the ceramic tiles’ surface defects. Pattern Recognition, 66, 174-189. [15] Chu, M.-X., Liu, X.-P., Gong, R.-F., & Liu, L.-M. (2018). Multi-class classification meth-od using twin support vector machines with multi-information for steel surface defects. Chemometrics And Intelligent Laboratory Systems, 176, 108-118. [16] Gong, R.-F., Wu, C.-D., & Chu, M.-X. (2018). Steel surface defect classification using multiple hyper-spheres support vector machine with additional information. Chemometrics And Intelligent Laboratory Systems, 172, 109-117. [17] Gong, R., Chu, M., Yang, Y., & Feng, Y. (2019). A multi-class classifier based on support vector hyper-spheres for steel plate surface defects. Chemometrics And Intelligent Laboratory Systems, 188, 70-78 [18] Zhao, X., La, K., & Dai, D. (2007). An Improved BP Algorithm and Its Application in Classification of Surface Defects of Steel Plate. Journal Of Iron And Steel Research Interna-tional, 14(2), 52-55. [19] Mukherjee, A., Ray, T., Chaudhuri, S., Dutta, P., Sen, S., & Patra, A. (2007). Image-based classification of defects in frontal surface of fluted ingot. Measurement, 40(6), 687-698. [20] Ke, C.-S., Hu, S.-P., & Yen, Y. (2014). Automatic recognition of surface defects on hot-rolled steel strip using scattering convolution network. Journal of Computational Information Systems, 10(7), 3049-3055. [21] Chu, M.-X., Zhao, J., Liu, X.-P., & Gong, R.F. (2017). Multi-class classification for steel surface defects based on machine learning with quantile hyper-spheres. Chemometrics And In-telligent Laboratory Systems, 168, 15-27. [22] Chen, W., Gao, Y., Gao, L., & Li, X. (2018). A New Ensemble Approach based on Deep Convolutional Neural Networks for Steel Surface Defect classification. Procedia CIRP, 72, 1069-1072. [23] Fu, G., Sun, P., Zhu, W., Yang, J., Cao, Y., Yang, M., & Cao, Y. (2019). A deep-learning-based approach for fast and robust steel surface de-fects classification. Optics and Lasers In En-gineering, 121, 397-405. [24] Deitsch, S., Christlein, V., Berger, S., Buerhop-Lutz, C., Maier, A., Gallwitz, F., & Riess, C. (2019). Automatic classification of defective photovoltaic module cells in electrolumines-cence images. Solar Energy, 185, 455-468. [25] Wei, F., Yao, G., Yang, Y., & Sun, Y. (2019). Instance-level recognition and quantification for concrete surface bughole based on deep learning. Automation In Construction, 107, 102920. [26] Hassan, S., Dang, L., Mehmood, I., Im, S., Choi, C., & Kang, J. et al. (2019). Under-ground sewer pipe condition assessment based on convolutional neural networks. Automation In Construction, 106, 102849. [27] Gao, Y., Gao, L., Li, X., & Yan, X. (2019). A semi-supervised convolutional neural net-work-based method for steel surface defect recognition. Robotics And Computer-Integrated Manufacturing, 61, 101825. [28] He, D., Xu, K., Zhou, P., & Zhou, D. (2019). Surface defect classification of steels with a new semi-supervised learning method. Optics And Lasers In Engineering, 117, 40-48. [29] Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. [30] 李宏毅 — Backpropagation :https://sakura-gh.github.io/ML-notes/ML-notes-html/9_Backpropagation.html [31] Tensorflow:https://www.tensorflow.org/ [32] Keras Github : https://github.com/keras-team/keras [33] OpenCv : https://opencv.org/ [34] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, É., 2011. Scikit-learn: Machine L [35] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. [36] 機器學習: Kernel 函數:https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-kernel-%E5%87%BD%E6%95%B8-47c94095171 [37] B.-M. Chang, H.-H. Tsai, X.-P. Lin and P.-T. Yu. (2010). Design of median-type filters with an impulse noise detector using decision tree and particle swarm optimization for image restoration. Computer Science And Information Systems, 7(4), 859-882. [38] Breiman, L. (2001). Random Forests . Machine Learning, 45(1), 5-32. [39] Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123-140. [40] Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In C.S. Mellish (Ed.), Proceedings of the 14th International Joint Conference on Artificial Intelligence (pp. 1137–1143). Morgan Kaufmann. [41] Song, K., & Yan, Y. (2013). A noise robust method based on completed local binary pat-terns for hotrolled steel strip surface defects. Applied Surface Science, 285, 858-864. [42] DAGM (Deutsche Arbeitsgemeinschaft für Mustererkennung e.V., German chapter of the IAPR (International Association for Pattern Recognition)) and the GNSS (German Chapter of the European Neural Network Society)(2007) Weakly Supervised Learning for Indus trial Opti-cal Inspection, DAGM symposium, https://hci.iwr.uni-heidelberg.de/node/3616 [43] Li, Y., Li, G.-Y., & Jiang, M.-M. (2016). An End-to-End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks. Steel Research International, 88(2). [44] Haralick, R., Shanmugam, K. and Dinstein, I., 1973. Textural Features for Image Classifi-cation. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), pp.610-621.
|