Open Access
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
Article Number 01006
Number of page(s) 6
Section Session 1: Robotics
Published online 05 September 2017
  1. 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,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 48, no. 9, pp. 3446–3456, 2010. [CrossRef] [Google Scholar]
  2. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 1. IEEE, 2005, pp. 886–893. [CrossRef] [Google Scholar]
  3. X. X. Bao, S. S. Zinger, D. P. P. With, R. R. Wijnhoven, and J. J. Han, “Water region and multiple ship detection for port surveillance,” in Proc. the 33rd WIC Symposium on Information Theory in the Benelux, 2012. [Google Scholar]
  4. Gan Lu, P. Liu, and L. Wang. “Rotation Sliding Window of the Hog Feature in Remote Sensing Images for Ship Detection,” International Symposium on Computational Intelligence and Design IEEE, 2015:401–404. [Google Scholar]
  5. Krizhevsky, Alex, I. Sutskever, and G. E. Hinton. “ImageNet classification with deep convolutional neural networks,” International Conference on Neural Information Processing Systems Curran Associates Inc. 2012:1097–1105. [EDP Sciences] [Google Scholar]
  6. Zhu H., et al. “Orientation robust object detection in aerial images using deep convolutional neural network,” IEEE International Conference on Image Processing IEEE, 2015:3735–3739. [Google Scholar]
  7. J. Tang, C. Deng, and G. B. Huang. “Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine,” IEEE Transactions on Geoscience & Remote Sensing 53.3(2015):1174–1185. [Google Scholar]
  8. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, Dec. 2006. [CrossRef] [Google Scholar]
  9. L. Zhang, L. Zhang, and B. Du. “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art,” IEEE Geoscience & Remote Sensing Magazine 4.2(2016):22–40. [CrossRef] [Google Scholar]
  10. R. Girshick, J. Donahue, T. Darrell, et al. “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Transactions on Pattern Analysis & Machine Intelligence 38.1(2016):142–158. [CrossRef] [Google Scholar]
  11. J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders. “Selective Search for Object Recognition[J],” International Journal of Computer Vision, 2013, 104(2):154–171. [Google Scholar]
  12. C. L. Zitnick and P. Dollár. “Edge Boxes: Locating Object Proposals from Edges,” 8693(2014):391–405. [Google Scholar]
  13. M. M. Cheng, Z. Zhang, W. Y. Lin, et al. Cheng Ming Ming, et al. “BING: Binarized Normed Gradients for Objectness Estimation at 300fps,” (2014):3286–3293. [Google Scholar]
  14. K. He, X. Zhang, S. Ren, et al. “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,” He Kaiming, et al. “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.” IEEE Transactions on Pattern Analysis & Machine Intelligence 37.9(2015):1904–1916. [Google Scholar]
  15. R. Girshick. “Fast R-CNN,” Proceedings of the IEEE International Conference on Computer Vision. 2015: 1440–1448. [Google Scholar]
  16. S. Ren, K. He, R . Girshick, et al. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J],” Computer Science, 2015:1–1. [Google Scholar]
  17. J. Redmon, S. Divvala, R. Girshick, et al. “You Only Look Once: Unified, Real-Time Object Detection[J],” Computer Science, 2015. [Google Scholar]
  18. W. Liu, D. Anguelov, D. Erhan, et al. “SSD: Single Shot MultiBox Detector[J]. 2016. “SSD: Single Shot MultiBox Detector[J],” (2016). [Google Scholar]
  19. M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and transferring mid-level image representations using convolutional neural networks,” in Proc. IEEE Comput. Vis. Pattern Recog., 2014, pp. 1717–1724. [Google Scholar]
  20. S. Pan and Q. Yang. “A survey on transfer learning. Knowledge and Data Engineering,” IEEE Transactions on, 22(10):1345–1359, 2010. 2 [Google Scholar]
  21. K. Simonyan, A. Zisserman. “Very deep convolutional networks for large-scale image recognition,” Computer Science, 2015. [Google Scholar]

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