Machine Vision Inspection Systems, Machine Learning-Based Approaches. Группа авторов
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Lehtonen, E. and Laiho, M., CNN using memristors for neighborhood connections, in: 12th International Workshop on Cellular Nanoscale Networks and their Applications, IEEE, pp. 1–4, 2010.
11. Hinton, G.E., Krizhevsky, A., Wang, S.D., Transforming auto-encoders, in: International Conference on Artificial Neural Networks, Springer, pp. 44–51, 2011.
12. Sethy, A., Patra, P.K., Nayak, S.R., Offline Handwritten Numeral Recognition Using Convolution Neural Network, in: Machine Vision Inspection Systems: Image Processing, Concepts, Methodologies and Applications, M. Malarvel, S.R. Nayak, S.N. Panda, P.K. Pattnaik, N. Muangnak (Eds.), cp. 9, pp. 197–212, John Wiley & Sons Inc, New York, United States, 2020.
13. Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P., Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens., 54, 6232–6251, 2016.
14. Garcia-Gasulla, D., Pares, F., Vilalta, A., Moreno, J., Ayguadé, E., Labarta, J., Cortés, U., Suzumura, T., On the behavior of convolutional nets for feature extraction. J. Artif. Intell. Res., 61, 563–592, 2018.
15. Liu, B., Yu, X., Zhang, P., Yu, A., Fu, Q., Wei, X., Supervised deep feature extraction for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens., 56, 1909–1921, 2017.
16. Sethy, A., Patra, P.K., Nayak, S.R., Nayak, D.R., A Gabor Wavelet Based Approach for Off-Line Recognition of ODIA Handwritten Numerals. Int. J. Eng. Technol., 7, 253–257, 2018.
17. Krüger, V. and Sommer, G., Gabor wavelet networks for object representation, in: Multi-Image Analysis, R. Klette, G. Gimel’farb, T. Huang (Eds.), LNCS, 2032, pp. 115–128, Springer, Berlin, Heidelberg, 2001.
18. Kaushal, A. and Raina, J., Face detection using neural network & Gabor wavelet transform. Int. J. Comput. Sci. Technol., 1, 58–63, 2010.
19. Nayak, S.R., Mishra, J., Palai, G., Analysing roughness of surface through fractal dimension: A review. Image Vision Comput., 89, 21–34, 2019.
20. Nayak, S.R., Mishra, J., Palai, G., A modified approach to estimate fractal dimension of gray scale images. Optik, 161, 136–145, 2018.
21. Nayak, S., Khandual, A., Mishra, J., Ground truth study on fractal dimension of color images of similar texture. J. Text. Inst., 109, 1159–1167, 2018.
22. Sethy, A. and Patra, P.K., Off-line Odia Handwritten Character Recognition: an Axis Constellation Model Based Research. Int. J. Innov. Technol. Explor. Eng., 8, 788–793, 2019.
23. Zhang, J., Zhu, Y., Du, J., Dai, L., Radical analysis network for zero-shot learning in printed Chinese character recognition, in: IEEE International Conference on Multimedia and Expo, IEEE, pp. 1–6, 2018.
24. Bertinetto, L., Henriques, J.F., Valmadre, J., Torr, P., Vedaldi, A., Learning feed-forward one-shot learners, in: 30th International Conference on Neural Information Processing Systems, ACM, pp. 523–531, 2016.
25. Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H., Fully- convolutional siamese networks for object tracking, in: European Conference on Computer Vision, Springer, pp. 850–865, 2016.
26. Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T., One- shot learning with memory-augmented neural networks, arXiv preprint arXiv:1605.06065, 1–13, 2016.
27. Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., Matching networks for one shot learning, in: 30th International Conference on Neural Information Processing Systems, pp. 3630–3638, 2016.
28. Bromley, J., Guyon, I., Lecun, Y., Säckinger, E., Shah, R., Signature verification using a “Siamese” time delay neural network, in: 6th International Conference on Neural Information Processing Systems, pp. 737–744, 1993.
29. Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T., Metalearning with memory-augmented neural networks, in: International Conference on Machine Learning, pp. 1842–1850, 2016.
30. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition, in: IEEE, vol. 86, pp. 2278–2324, 1998.
31. Kumar, A.D., Novel deep learning model for traffic sign detection using capsule networks, arXiv preprint, arXiv:1805.04424, 1–5, 2018.
32. Zhao, W., Ye, J., Yang, M., Lei, Z., Zhang, S., Zhao, Z., Investigating capsule networks with dynamic routing for text classification, arXiv preprint, arXiv:1804.00538, 1–12, 2018.
33. Lalonde, R. and Bagci, U., Capsules for object segmentation, arXiv preprint, arXiv:1804.04241, 1–9, 2018.
34. Rajasegaran, J., Jayasundara, V., Jayasekara, S., Jayasekara, H., Seneviratne, S., Rodrigo, R., Deepcaps: Going deeper with capsule networks, in: IEEE Conference on Computer Vision and Pattern Recognition, pp. 10725–10733, 2019.
35. Xu, B., Wang, N., Chen, T., Li, M., Empirical evaluation of rectified activations in convolutional network, arXiv preprint, arXiv:1505.00853, 1–5, 2015.
36. Mackay, D.J. and Mac Kay, D.J., Information theory, inference and learning algorithms, Cambridge Universtity Press, Cambridge, United Kingdom, 2003.
37. Kingma, D.P. and Ba, J., Adam: A method for stochastic optimization, arXiv preprint, arXiv:1412.6980, 1–15, 2014.
38. Jarrett, K., Kavukcuoglu, K., Ranzato, M.A., Lecun, Y., What is the best multistage architecture for object recognition?, in: 12th international conference on computer vision, IEEE, pp. 2146–2153, 2009.
39. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J., Convolutional neural network committees for handwritten character classification, in: International Conference on Document Analysis and Recognition, IEEE, pp. 1135–1139, 2011.
40. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., Imagenet: A large- scale hierarchical image database, in: IEEE Conference on computer vision and pattern recognition, IEEE, pp. 248–255, 2009.
41. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., Microsoft coco: Common objects in context, in: European Conference on Computer Vision, Springer, pp. 740–755, 2014.
1 * Corresponding author: [email protected]
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