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All issues Volume 12 (2017) ITM Web Conf., 12 (2017) 05012 References
Open Access
Issue
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
Article Number 05012
Number of page(s) 6
Section Session 5: Information Processing Methods and Techniques
DOI https://doi.org/10.1051/itmconf/20171205012
Published online 05 September 2017
  1. G. Dahl, D. Yu, L. Deng, and A. Acero, “Contextdependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition, “ IEEE Transactions on Audio Speech & Language Processing, vol. 20, Jan. 2012, pp. 30–42, doi: 10.1109/TASL.2011.2134090. [CrossRef] [Google Scholar]
  2. D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber, “Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition, “ CoRR, vol. 22, Nov. 2010, pp. 3207–3220, doi: 10.1162/NECO_a_00052. [Google Scholar]
  3. R. Collobert and J. Weston, “A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, “ International Conference on Machine learning (ICML 08), ACM press, Jul. 2008, pp. 160–167, doi: 10.1145/1390156.1390177. [CrossRef] [Google Scholar]
  4. R. Raina, A. Madhavan, and A. Y. Ng, “Large-scale Deep Unsupervised Learning Using Graphics Processors, “ International Conference on Machine Learning (ICML 09), ACM press, Jun. 2009, pp. 873–880, doi: 10.1145/1553374.1553486. [Google Scholar]
  5. Z. Y. Han and J. S. Chong, “A Review of Ship Detection Algorithms in Polarimetric SAR Images, “ International Conference on Signal Processing (ICSP 04), IEEE press, vol. 3, Sept. 2004, pp. 2155–2158, doi: 10.1109/ICOSP.2004.1442203. [Google Scholar]
  6. K. Eldhuset, “An Automatic Ship and Ship Wake Detection System for Spaceborne SAR Images in Coastal Regions, “ IEEE Transaction on Geoscience and Remote Sensing, vol. 34, Jul. 1996, pp. 1010–1019, doi: 10.1109/36.508418. [CrossRef] [Google Scholar]
  7. H. Greidanus, P. Clayton, N. Suzuki, and P. Vachon, “Benchmarking Operational SAR Ship Detection, “ International Geoscience and Remote Sensing Symposium (IGARSS 04), IEEE press, vol. 6, Dec. 2004, pp. 4215–4218, doi: 10.1109/IGARSS.2004.1370065. [Google Scholar]
  8. C. C. Wackerman, K. S. Friedman, and X. Li, “Automatic Detection of Ships in RADARSAT-1 SAR Imagery, “ Canadian Journal of Remote Sensing, vol. 27, Jul. 2014, pp. 568–577, doi: 10.1080/07038992.2001.10854896. [CrossRef] [Google Scholar]
  9. D. J. Crisp, “The State of the Art in Ship Detection in Synthetic Aperture Radar Imagery, “ Organic Letters, vol. 35, May 2004, pp. 2165–2168. [Google Scholar]
  10. C. Zhu, H. Zhou, R. Wang and J. Guo, “A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features, “ IEEE Transactions on Geoscience and Remote Sensing, vol. 48, Sept. 2010, pp. 3446–3456, doi: 10.1109/TGRS.2010.2046330. [Google Scholar]
  11. J. Antelo, G. Ambrosio, and C. Galindo, “Ship Detection and Recognition in High-resolution Satellite Images, “ International Geoscience and Remote Sensing Symposium (IGARSS 09), IEEE press, vol. 4, Feb. 2010, pp. 514–517, doi: 10.1109/IGARSS.2009.5417426. [Google Scholar]
  12. H. Chen and X. Gao, “Ship Recognition based on Improved Forwards-backwards Algorithm, “ International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 09), IEEE press, vol. 5, Dec. 2009, pp. 509–513, doi: 10.1109/FSKD.2009.336. [Google Scholar]
  13. Q. Wang, X. Gao, and D. Chen, “Pattern Recognition for Ship Based on Bayesian Networks, “ International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 07), IEEE press, vol. 4, Aug. 2007, pp. 684–688, doi: 10.1109/FSKD.2007.447. [CrossRef] [Google Scholar]
  14. J. Tang, C. Deng, G.H. Huang, and B. Zhao, “Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine, “ IEEE Transactions on Geoscience and Remote Sensing, vol. 53, Jul. 2014, pp. 1174–1183, doi: 10.1109/TGRS.2014.2335751. [Google Scholar]
  15. R. C. Gonzalez and R. E. Woods, “Digital Image Processing, “ 3rd ed. Knoxville: Gatesmark, 2007, pp. 742–745. [EDP Sciences] [Google Scholar]
  16. V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines, “ International Conference on Machine Learning, (ICML 10), Proc icml, Jun. 2010, pp. 807–814. [EDP Sciences] [Google Scholar]
  17. Krizhevsky A, Sutskever I and Hinton G, “ImageNet classification with deep convolutional neural networks, “ In NIPS, 2012. [Google Scholar]
  18. J. Gehring, Y. Miao, and A. Waibel, “Extracting Deep Bottleneck Features Using Stacked Auto-encoders, “ International Conference on Acoustics, Speech and Signal Processing (ICASSP 13), IEEE press, vol. 32, Oct. 2013, pp. 3377–3381, doi: 10.1109/ICASSP.2013.6638284. [Google Scholar]
  19. P. Vincent, H. Larochelle, and Y. Bengio, “Extracting and Composing Robust Features with Denoising Autoencoders, “ International Conference on MachineLearning (ICML 08), ACM press, Jul. 2008, pp. 1096–1103, doi: 10.1145/1390156.1390294. [EDP Sciences] [Google Scholar]
  20. M. Chen and Z. Xu, “Marginalized Denoising Autoencoders for Domain Adaptation, “ International Conference on Machine Learning (ICML 12), Computer Science, 2012. [Google Scholar]

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