Open Access
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
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) [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]. (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,) [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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.