A Two-Dimension Chaotic Sequence Generating Method and Its Application for Image Segmentation

  • Xue-Feng Zhang
  • Jiu-Lun Fan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


Chaotic optimization is a new optimization technique. For image segmentation, conventional chaotic sequence is not fit to two-dimension gray histogram because it is proportional distributing in [0,1]×[0,1]. In order to generate a chaotic sequence can be used to the optimization processing of image segmentation method in two-dimension gray histogram, we propose an chaotic sequence generating method based on Arnold chaotic system and Bézier curve generating algorithm. Simulation results show that the generated sequence is pseudorandom. The most important characteristic of this chaotic sequence is that its distribution is approximately inside a disc whose center is (0.5,0.5) , this characteristic indicates that the sequence is superior to the Arnold chaotic sequence in image segmenting. Based on the extended chaotic sequence generating method, we study the two-dimension Otsu’s image segmentation method using chaotic optimization. Simulation results show that the method using the extended chaotic sequence has better segmentation effect and lower computation time than the existed two-dimension Otsu’s method.


Control Point Image Segmentation Chaotic System Target Class Chaotic Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Lee, S.U., Chung, S.Y.: A comparative performance study of several global thresholding techniques for segmentation. Comput. Graph Image Process 52, 171–190 (1990)CrossRefGoogle Scholar
  2. 2.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. on Systems, Man and Cybernetics 9(1), 62–66 (1979)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Liu, J.-z., Li, W.-q.: Two-dimension Otsu’s Automatic Segmentation method of Gray Image. Automatization Journal 19(1), 101–105 (1993) (in Chinese)Google Scholar
  4. 4.
    Gong, J., Li, L.Y., Chen, W.N.: Fast recursive algorithm for two-dimensional thresholding. Pattern Recognition 31(3), 295–300 (1998)CrossRefGoogle Scholar
  5. 5.
    Hen, H., Merhav, N.: On the Threshold Effect in the Estimation of Chaotic Sequences. IEEE Trans. on Information Theory 50(11), 2894–2904 (2004)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Wang, L., Smith, K.: On chaotic simulated annealing. IEEE Trans. on Neural Networks 9(4), 716–718 (1998)CrossRefGoogle Scholar
  7. 7.
    Mirasso, C.R., et al.: Chaos Shift-Keying Encryption in Chaotic External-Cavity Semiconductor Lasers Using a Single-Receiver Scheme. IEEE Photonics Technology Letters 14(4), 456–458 (2002)CrossRefGoogle Scholar
  8. 8.
    Zhao, L., et al.: A Network of Globally Coupled Chaotic Maps for Adaptive Multi-Resolution Image Segmentation. In: Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN 2002) (2002)Google Scholar
  9. 9.
    Xiu, C.-b., et al.: Optimal Entropy Thresholding Image Segmentation Based on Chaos Optimization. Computer Engineering and Applications Journal 2004(27), 76–77 (in Chinese)Google Scholar
  10. 10.
    Piegl, L., Tiller, W.: The NURBS Book. Springer, Heidelberg (1995)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xue-Feng Zhang
    • 1
    • 2
  • Jiu-Lun Fan
    • 2
  1. 1.Department of Electronic EngineeringXidian UniversityXi’anP.R. China
  2. 2.Department of Information and ControlXi’an Institute of Post and TelecommunicationsXi’anP.R. China

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