Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 308164, 10 pages
http://dx.doi.org/10.1155/2014/308164
Research Article

Fast Threshold Selection Algorithm of Infrared Human Images Based on Two-Dimensional Fuzzy Tsallis Entropy

College of Computer Science, Guangxi University of Science and Technology, Liuzhou 545006, China

Received 4 October 2013; Accepted 11 December 2013; Published 16 January 2014

Academic Editor: Chung-Hao Chen

Copyright © 2014 Dong-xue Xia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P.-Y. Yin, “Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization,” Signal Processing, vol. 82, no. 7, pp. 993–1006, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Li, C. Liu, G. Liu, X. Yang, and Y. Cheng, “Statistical thresholding method for infrared images,” Pattern Analysis and Applications, vol. 14, no. 2, pp. 109–126, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. F. Nie, C. Gao, Y. Guo, and M. Gan, “Two-dimensional minimum local cross-entropy thresholding based on co-occurrence matrix,” Computers and Electrical Engineering, vol. 37, no. 5, pp. 757–767, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. J. F. Li, W. G. Gong, W. H. Li, and X. Liu, “Robust pedestrian detection in thermal infrared imagery using the wavelet transform,” Infrared Physics and Technology, vol. 53, no. 4, pp. 267–273, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Nie, C. Gao, and Y. Guo, “Infrared human image segmentation using fuzzy Havrda-Charv't entropy and chaos PSO algorithm,” Journal of Computer-Aided Design and Computer Graphics, vol. 22, no. 1, pp. 129–135, 2010. View at Google Scholar · View at Scopus
  6. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146–168, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Portes de Albuquerque, I. A. Esquef, A. R. Gesualdi Mello, and M. Portes de Albuquerque, “Image thresholding using Tsallis entropy,” Pattern Recognition Letters, vol. 25, no. 9, pp. 1059–1065, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. P. K. Sahoo and G. Arora, “Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy,” Pattern Recognition Letters, vol. 27, no. 6, pp. 520–528, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. A. De Luca and S. Termini, “A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory,” Information and Control, vol. 20, no. 4, pp. 301–312, 1972. View at Google Scholar · View at Scopus
  10. H. D. Cheng, C. H. Chen, H. H. Chiu, and H. Xu, “Fuzzy homogeneity approach to multilevel thresholding,” IEEE Transactions on Image Processing, vol. 7, no. 7, pp. 1084–1088, 1998. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Tao, H. Jin, and L. Liu, “Object segmentation using ant colony optimization algorithm and fuzzy entropy,” Pattern Recognition Letters, vol. 28, no. 7, pp. 788–796, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. H. D. Cheng, Y. H. Chen, and X. H. Jiang, “Thresholding using two-dimensional histogram and fuzzy entropy principle,” IEEE Transactions on Image Processing, vol. 9, no. 4, pp. 732–735, 2000. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Zhao, A. M. N. Fu, and H. Yan, “A technique of three-level thresholding based on probability partition and fuzzy 3-partition,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 3, pp. 469–479, 2001. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Eusuff, K. Lansey, and F. Pasha, “Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization,” Engineering Optimization, vol. 38, no. 2, pp. 129–154, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. E. Elbeltagi, T. Hegazy, and D. Grierson, “Comparison among five evolutionary-based optimization algorithms,” Advanced Engineering Informatics, vol. 19, no. 1, pp. 43–53, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. J. W. Davis and M. A. Keck, “A two-stage template approach to person detection in thermal imagery,” in Proceedings of the 7th IEEE Workshop on Applications of Computer Vision (WACV '05), pp. 364–369, January 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. V. Sharma and J. W. Davis, “Simultaneous detection and segmentation of pedestrians using top-down and bottom-up processing,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), pp. 3666–3673, June 2007. View at Publisher · View at Google Scholar · View at Scopus