Open Access
Issue
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 01008
Number of page(s) 3
Section Network Security System, Neural Network and Data Information
DOI https://doi.org/10.1051/matecconf/201823201008
Published online 19 November 2018
  1. Katsaggelos. A. K. Digital image restoration. (Berlin: Springer Publishing 2012) [Google Scholar]
  2. Fahmy M F, Raheem G M A, Mohamed U S. A new fast iterative blind deconvolution algorithm. Journal of Signal and Information Processing. 3(1).(2012) [CrossRef] [Google Scholar]
  3. Zhang. H, Wipf. D, Zhang. Y. Multi-observation blind deconvolution with an adaptive on parse prior. IEEE transactions Pattern Analysis and Machine Intelligence 36(8): 1628-1643(2014) [CrossRef] [Google Scholar]
  4. Ponomarenko. N, Jin. L, Ieremeiev. O. Image database TID 2013: Peculiarities, results and perspectives. Signal Processing: Image Communication. 30:57-77 (2015) [CrossRef] [Google Scholar]
  5. Farooq. U, Shen. T. Z, Zhao. S Y. Image restoration by using new AGA optimizd BPNN. Procedia Engineering. 29(4): 3028-3032 (2012) [CrossRef] [Google Scholar]
  6. Zhang. Y. Q, Wang. X. Y. A symmetric image encryption algorithm based on mixed Linear-nonlinear coupled map lattice. Information Sciences. 273(8): 329-351 (2012) [CrossRef] [Google Scholar]
  7. Wang. X, Wang. S, Wang. Z. A new key agreement protocol based on Chebyshev chaotic maps. Security & Communication Networks. 9(18) (2016) [Google Scholar]
  8. Bouvrie. J. Notes on convolutional neural networks[EB/OL]. http://web.mit.edu/jvb/www/papers/cnn_tutorial.pdf (2016]. ) [Google Scholar]
  9. Wang Xingyuan, Luan Dapeng. A secure key agreement protocol based on chaotic maps[J]. Chinese Physics B, 2013, 22(11): 239-243. [Google Scholar]
  10. Shengzhi. D, Zengqiang. C, Zhuzhi. Y. Sensitivity to noise in bi-directional associative memory (BAM). IEEE trans. on NEURAL NETWORKS. 16(7):887-898 (2015) [Google Scholar]
  11. Zhong. Y. Intrinsic shape signatures:A shape descriptor for 3D object recognition. 2009 IEEE 12th International Con-ference on Computer Vision Workshops (ICCV Workshops). IEEE, 2009:689-696 (2009) [Google Scholar]
  12. Guo. Y, Sohel. F,Bennamoun. M. Rotational projection statistics for 3D local surface description and object recognition. International Journal of Computer Vision. 105(1):63-86 (2013) [CrossRef] [Google Scholar]
  13. Guo. Y, Sohel. F, Bennamoun. M. A novel local surface feature for 3D object recognition under clutter and occlusion. Information Sciences. 293:196-213 (2015) [CrossRef] [Google Scholar]
  14. Guo. Y, Bennamoun. M, Sohel. F. A comprehensive performance evaluation of 3D local feature descriptors. International Journal of Computer Vision. 116(1): 66-89 (201,) [CrossRef] [Google Scholar]
  15. Tombari. F, Salti. S, Di Stefano. L. Unique signatures of histograms for local surface description. European Conference on Computer Vision. Springer Berlin Heidelberg, 2010:356-369. [Google Scholar]
  16. Salti. S, Tombari. F, Di Stefano. L. Shot: unique signatures of histograms for surface and texture description. Computer Vision and Image Understanding. 125:251-264 (2014) [CrossRef] [Google Scholar]
  17. Taati. B, Greenspan. M. Local shape descriptor selection for object recognition in range data. Computer Vision and Image Understanding. 115(5): 681-694 (2011) [CrossRef] [Google Scholar]
  18. Prkahya. S. M, Liu. B, Lin. W. B-Shot:A binary feature descriptor for fast and efficient keypoint matching on 3D point clouds. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015:1929-1934. [Google Scholar]
  19. Rusu. R. B, Blodow. N, Marton. Z C. Aligning point cloud views using persistent feature histograms. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2008:3384-3391. [Google Scholar]
  20. Rusu. R. B, Blodow. N, Beetz. M. Fast point feature histograms (FPFH) for 3D registration. IEEE International Conference on Robotics and Automation, 2009(ICRA 09). IEEE, 2009: 3212-3217. [Google Scholar]

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