Open Access
Issue |
MATEC Web Conf.
Volume 220, 2018
2018 The 2nd International Conference on Mechanical, System and Control Engineering (ICMSC 2018)
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Article Number | 10004 | |
Number of page(s) | 8 | |
Section | Modern information technology and application | |
DOI | https://doi.org/10.1051/matecconf/201822010004 | |
Published online | 29 October 2018 |
- Mignotte, M.; Collet, C.; Perez, P.; Bouthemy, P. Sonar image segmentation using an unsupervised hierarchical mrf model. IEEE Transactions on Image Processing, 9, 1216-31, Publisher Item Identifier S 1057-7149(00)05669-4, (2000). [CrossRef] [Google Scholar]
- Chabane, A. N.; Islam, N.; Zerr, B. Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory. Ocean Engineering, 2017, 142, 133-144. Available online: http://dx.doi.org/10.1016/j.oceaneng/ (accessed on 05 July 2017). [CrossRef] [Google Scholar]
- Li, Q.; Ma, G.; Huo, G.; Yan, Z.; Sheng, H. New segmentation method of side-scan sonar image based on edge detection in NSCT domain. Chinese Journal of Scientific Instrument, 34, 1795-1801, DOI: 10.19650/j.cnki.cjsi.2013.08.016, (2013). [Google Scholar]
- Collet, C.; Thourel, P.; Perez, P.; Bouthemy, P. Hierarchical MRF modeling for sonar picture segmentation. In Processings of the 3rd IEEE International Conference on Image Processing; Lausanne, Switzerland, September pp. 979-982,(1996). [Google Scholar]
- Samiee, K.; Rad, G. A. R. Textural Segmentation of Sidescan Sonar Images Based on Gabor Filters Bank and Active Contours without Edges. In Processings of the IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp. 3-8,(2008). [Google Scholar]
- Huo, G.; Yang, S. X.; Li, Q.; Zhou, Y. A Robust and Fast Method for Sidescan Sonar Image Segmentation Using Nonlocal Despeckling and Active Contour Model. IEEE Transactions on Cybernetics, 47, 855-872, DOI: 10.1109/TCYB, (2016) [CrossRef] [Google Scholar]
- Le, Q. V. Building high-level features using large scale unsupervised learning. In Processings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8595-8598,(2013) [Google Scholar]
- Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D. Going deeper with convolutions. In Processings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9,(2015) [Google Scholar]
- Girshick, R.; Donahue, J.; Darrell, T.; Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. Computer Science, 580-587, DOI: 10.1109/CVPR.2014, (2013) [Google Scholar]
- P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. Eprint Arxiv, arXiv:1312.6229v4,(2013) [Google Scholar]
- Long, J.; Zhang, N.; Darrell, T. Do Convnets Learn Correspondence?. Advances in Neural Information Processing Systems, 2, 1601-1609, arXiv:1411.1091v1,(2014) [Google Scholar]
- Zhang, N.; Donahue, J.; Girshick, R.; Darrell, T. Part-Based R-CNNs for Fine-Grained Category Detection. 8689, 834-849, arXiv:1407.3867v1, (2014) [Google Scholar]
- Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324,( 1998) [CrossRef] [Google Scholar]
- Krizhevsky, A.; Sutskever, I.; Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Processings of the International Conference on Neural Information Processing Systems, pp. 1097-1105,(2012) [Google Scholar]
- Wang, L.; Guo, S.; Huang, W.; Qiao, Y. Places205-VGGNet Models for Scene Recognition. Computer Science, arXiv:1508.01667v1,(2015) [Google Scholar]
- Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D. Going deeper with convolutions. In Processings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, (2014) [Google Scholar]
- He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition., 770-778, arXiv:1512.03385v1, (2015) [Google Scholar]
- Shen, D.; Wu, G.; Suk, H. I. Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering, 19, 221, DOI: 10.1146/annurev-bioeng-071516-044442,(2017) [Google Scholar]
- Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In Processings of the IEEE Conference on Computer Vision and Pattern Recognitio, pp. 3431-3440., (2015) [Google Scholar]
- LeCun, Y.; Boser, B.; Denker, J.S.; Henderson, D.; Howard, R.E.; Hubbard, W.; Jackel, L.D. Back propagation applied to handwritten zip code recognition. Neural Comput. 1, 541-551, DOI: 10.1162/neco.1989.1.4.541, (1989). [CrossRef] [Google Scholar]
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