Open Access
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
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] [EDP Sciences]
  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.
  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]
  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.
  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.
  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]
  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.
  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]
  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.
  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. [CrossRef]
  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.
  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.
  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]
  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. [CrossRef]
  15. R. C. Gonzalez and R. E. Woods, “Digital Image Processing, “ 3rd ed. Knoxville: Gatesmark, 2007, pp. 742–745. [EDP Sciences]
  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]
  17. Krizhevsky A, Sutskever I and Hinton G, “ImageNet classification with deep convolutional neural networks, “ In NIPS, 2012.
  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.
  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]
  20. M. Chen and Z. Xu, “Marginalized Denoising Autoencoders for Domain Adaptation, “ International Conference on Machine Learning (ICML 12), Computer Science, 2012.

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