Leather defect classification and segmentation using deep learning architecture

References

• Ahonen, T., A. Hadid, and M. Pietik¨ainen. 2004. “Face Recognition with Local Binary Patterns.” In European conference on computer vision, 469–481. Berlin, Heidelberg: Springer.
   Google Scholar
• Bong, H.-Q., Q.-B. Truong, H.-C. Nguyen, and M.-T. Nguyen. 2018. “Vision-based Inspection System for Leather Surface Defect Detection and Classification.” In 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), 300–304. Ho Chi Minh City: IEEE.
   Google Scholar
• Chen, L.-C., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018. “Encoder-decoder with Atrous Separable Convolution for Semantic Image Segmentation.” In Proceedings of the European conference on computer vision (ECCV), 801–818. Munich, Germany.
   Google Scholar
• Cortes, C., and V. Vapnik. 1995. “Support-vector Networks.” Machine Learning 20 (3): 273–297. doi:10.1007/BF00994018
   Web of Science ®Google Scholar
• Cover, T., and P. Hart. 1967. “Nearest Neighbor Pattern Classification.” IEEE Transactions on Information Theory 13 (1): 21–27. doi:10.1109/TIT.1967.1053964
   Web of Science ®Google Scholar
• Deng, J., W. Dong, R. Socher, L. Li-Jia, L. Kai, and L. Fei-Fei. 2009. “Imagenet: A Large-scale Hierarchical Image Database.” In 2009 IEEE conference on computer vision and pattern recognition, 248–255. Miami, FL: IEEE.
   Google Scholar
• Haralick, R. M., K. Shanmugam, and H. Dinstein. 1973. “Textural Features for Image Classification.” IEEE Transactions on Systems, Man, and Cybernetics 6 (6): 610–621. doi:10.1109/TSMC.1973.4309314
   Google Scholar
• He, Kaiming,, G. Gkioxari, P. Doll´ar, and R. Girshick. 2017. “Mask R-cnn.” Proceedings of the IEEE international conference on computer vision, 2961–2969. Venice, Italy
   Google Scholar
• Huang, G.-B., and L. Chen. 2007. “Convex Incremental Extreme Learning Machine.” Neurocomputing 70 (16–18): 3056–3062. doi:10.1016/j.neucom.2007.02.009
   Web of Science ®Google Scholar
• Huang, G.-B., Q.-Y. Zhu, and C.-K. Siew. 2006. “Extreme Learning Machine: Theory and Applications.” Neurocomputing 70 (1–3): 489–501. doi:10.1016/j.neucom.2005.12.126
   Web of Science ®Google Scholar
• Jain, A. K., P. W. D. Robert, and J. Mao. 2000. “Statistical Pattern Recognition: A Review.” IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (1): 4–37. doi:10.1109/34.824819
   Web of Science ®Google Scholar
• Jawahar, M., N. K. Chandra Babu, and K. Vani. 2014. “Leather Texture Classification Using Wavelet Feature Extraction Technique.” In 2014 IEEE International Conference on Computational Intelligence and Computing Research, 1–4. Coimbatore: IEEE.
   Google Scholar
• Jian, L., H. Wei, and H. Bin. 2010. “Research on Inspection and Classification of Leather Surface Defects Based on Neural Network and Decision Tree.” In 2010 International Conference On Computer Design and Applications, Vol. 2, V2–381. Qinhuangdao: IEEE.
   Google Scholar
• Ju´nior, D. S., A. Holanda, F. Corona, G. A. Barreto, Y. Miche, and A. Lendasse. 2015. “Minimal Learning Machine: A Novel Supervised Distance-based Approach for Regression and Classification.” Neurocomputing 164: 34–44. doi:10.1016/j.neucom.2014.11.073
   Web of Science ®Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems, 1097–1105, edited by F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, 1097–1105. Curran Associates, Inc.
   Google Scholar
• Kwon, J.-W., Y.-Y. Choo, H.-H. Choi, J.-M. Cho, and K. Gyung-Suk 2004. “Development of Leather Quality Discrimination System by Texture Analysis.” In 2004 IEEE Region 10 Conference TENCON 2004, 327–330. Chiang Mai: IEEE.
   Google Scholar
• Liong, S.-T., Y. S. Gan, Y.-C. Huang, K.-H. Liu, and W.-C. Yau. 2019a. “Integrated Neural Network and Machine Vision Approach for Leather Defect Classification.” arXiv Preprint arXiv:1905.11731.
   Google Scholar
• Liong, S.-T., Y. S. Gan, Y.-C. Huang, C.-A. Yuan, and H.-C. Chang. 2019b. “Automatic Defect Segmentation on Leather with Deep Learning.” arXiv Preprint arXiv:1903.12139.
   Google Scholar
• Nair, V., and G. E. Hinton. 2010. “Rectified Linear Units Improve Restricted Boltzmann Machines.” In Proceedings of the 27th international conference on machine learning (ICML10), 807–814. Haifa, Israel.
   Google Scholar
• Park, J., and I. W. Sandberg. 1991. “Universal Approximation Using Radial-basisfunction Networks.” Neural Computation 3 (2): 246–257. doi:10.1162/neco.1991.3.2.246
   PubMed Web of Science ®Google Scholar
• Pereira, R. F., M. L. D. Dias, C. M. D. S. Medeiros, and P. P. R. Filho. n.d. “Classification of Failures in Goat Leather Samples Using Computer Vision and Machine Learning.”
   Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-net: Convolutional Networks for Biomedical Image Segmentation.” In International Conference on Medical image computing and computer-assisted intervention, 234–241. Munich, Germany: Springer.
   Google Scholar
• Suykens, J. A. K., and J. Vandewalle. 1999. “Least Squares Support Vector Machine Classifiers.” Neural Processing Letters 9 (3): 293–300. doi:10.1023/A:1018628609742
   Web of Science ®Google Scholar
• Villar, P., M. Mora, and P. Gonzalez. 2011. “A New Approach for Wet Blue Leather Defect Segmentation.” In Iberoamerican Congress on Pattern Recognition, 591–598. Pucón - Chile: Springer.
   Google Scholar