Chenming Li, Hongmin Gao, Yao Yang, Xiaoyu Qu, and Wenjing Yuan


  1. [1] X. Xu, X. Li, B. Lei, and K. Lv, Unsupervised color imagesegmentation with color-alone feature using region growingpulse coupled neural network, Neurocomputing, 306(2018),2018, 1–16.
  2. [2] D. Zhou and Y. Shao, Region growing for image segmentationusing an extended PCNN model, Iet Image Processing, 12(5),2018, 729–737.
  3. [3] J. Shen, L. Han, M. Xu, C. Huang, Z. Zhang, and H. Wang,Focused-region segmentation for refocusing images from lightfields, Journal of Signal Processing Systems, 90(8–9), 2018,1–13.
  4. [4] J. Chen, B. Guan, H. Wang, X. Zhang, Y. Tang, and W. Hu,Image thresholding segmentation based on two dimensionalhistogram using gray level and local entropy information, IEEEAccess, 6(99), 2017, 1–1.
  5. [5] Z. Liang and Y. Chen, Closed-loop detection algorithm usingvisual words, International Journal of Robotics and Automation, 29(2), 2014. 10.2316/Journal.206.2014.2.206-3857.
  6. [6] L. Wang, X.F. Ye, G. Wang, and L. Wang, A fast hierarchi-cal MRF sonar image segmentation algorithm, InternationalJournal of Robotics and Automation, 32(1), 2017, 48–54.
  7. [7] F. Zhao, H. Liu, J. Fan, C.W. Chen, R. Lan, and N. Li,Intuitionistic fuzzy set approach to multi-objective evolutionary clustering with multiple spatial information for imagesegmentation, Neurocomputing, 312(2018), 2018, 296–309.
  8. [8] S.C. Satapathy, N.S.M. Raja, V. Rajinikanth, A.S. Ashour,and N. Dey, Multi-level image thresholding using Otsu andchaotic bat algorithm, Neural Computing and Applications,29(12), 2016, 1–23.
  9. [9] S. Rapaka and P.R. Kumar, Efficient approach for non-idealiris segmentation using improved particle swarm optimisation-based multilevel thresholding and geodesic active contours, IetImage Processing, 12(10), 2018, 1721–1729.
  10. [10] J. Li, W. Tang, J. Wang, and X. Zhang, Multilevel thresholdingselection based on variational mode decomposition for imagesegmentation, Signal Processing, 147(2018), 2018, 80–91.
  11. [11] Y. Shen, X. Zhang, K. Han, and J. Jiang, Research of imagesegmentation technology based on PCNN, Modern ElectronicsTechnique, 2014(2), 2014, 38–41.
  12. [12] S. Wei, Q. Hong, and M. Hou, Automatic image segmentation based on PCNN with adaptive threshold time constant,Neurocomputing, 74(9), 2011, 1485–1491.
  13. [13] X. Zhou, Study of image segmentation based on pulse coupledneural networks (Beijing, China: Beijing: Jiaotong University,2016).
  14. [14] D. Zhou, C. Gao, and Y. Guo, A coarse-to-fine strategy foriterative segmentation using simplified pulse-coupled neuralnetwork, Soft Computing, 18(3), 2014, 557–570.
  15. [15] A.G. Szekely and T. Lindblad, Parameter adaptation in asimplified pulse-coupled neural network, the workshop on virtual intelligence/dynamic neural networks: Neural networksfuzzy systems. International Society for Optics and Photonics,3728(1999), 1999, 278–285.
  16. [16] D. Zhou, H. Zhou, C. Gao, and Y. Guo, Simplified parametersmodel of PCNN and its application to image segmentation,Pattern Analysis and Applications, 19(4), 2016, 939–951.
  17. [17] Z. Xiao, J. Shi, and Q. Chang, Image segmentation withsimplified PCNN, international congress on image and signalprocessing. IEEE, 2009, 1–4.
  18. [18] Y. Na, H. Chen, L.I. Yanfeng, and X. Hao, Coupled parameter optimization of PCNN model and vehicle image segmentation, Journal of Transportation Systems Engineering andInformation Technology, 12(1), 2012, 48–54.
  19. [19] Y. Chen, S.K. Park, Y. Ma, and R. Ala, A new automaticparameter setting method of a simplified PCNN for imagesegmentation, IEEE Transactions on Neural Networks, 22(6),2011, 880–892.
  20. [20] G. Huang, Research on image segmentation algorithm based onpulse coupled neural network (Xian, China: Xidian University,2013).
  21. [21] M.A. Yi-De, R.L. Dai, and L.I. Lian, Automated image segmentation using pulse coupled neural networks and image’sentropy, Journal of China Institute of Communications, 23(1),2002, 46–50.
  22. [22] C.H. Li and C.K. Lee, Minimum cross entropy thresholding,Pattern Recognition, 26(4), 1993, 617–625.
  23. [23] C.H. Li and P.K.S. Tam, An iterative algorithm for minimumcross entropy thresholding, Pattern Recognition Letters, 19(8),1998, 771–776.
  24. [24] A.D. Brink and N.E. Pendock, Minimum cross-entropy thresh-old selection, Pattern Recognition, 29(1), 1996, 179–188.
  25. [25] R. Nie, Research on theory analysis and applications for criticalcharacteristics pulse coupled neural network (Yunnan, China:Yunnan University, 2013).

Important Links:

Go Back