Advertisement

Contourlet-Based Fusion Method for Video Surveillance Using the Pulse Coupled Neural Networks Model

  • Xi Cai
  • Guang Han
  • Jinkuan Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 308)

Abstract

To improve the quality of images in monitoring videos, an image fusion method using pulse coupled neural networks in the contourlet domain is presented. This work is mainly for combining useful information of multiple monitoring sensors to gain a more comprehensive description of the scene. After contourlet decomposition, normalized contourlet coefficients of source images were inputted into the neurons of the pulse coupled neural networks. Through this process, the output pulses of these networks reflected the perception of human visual neural systems to detailed information of the scene. Since a stronger stimulus could make corresponding neuron fire earlier and fire for more times, both the first firing time and the average times of ignition in the linking neighborhood centered at corresponding neuron were selected as the salience measures for the high-frequency fusion rule. Experimental results prove the validity of our proposed method both visually and objectively.

Keywords

Image Fusion Pulse Coupled Neural Networks Contourlet Transform 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yang, B., Jing, Z.L., Zhao, H.T.: Review of Pixel-Level Image Fusion. J. Shanghai Jiaotong Univ. Sci. 15, 6–12 (2010)CrossRefGoogle Scholar
  2. 2.
    Toet, A., Hogervorst, M.A., Nikolov, S.G., et al.: Towards Cognitive Image Fusion. Inf. Fusion 11, 95–113 (2010)CrossRefGoogle Scholar
  3. 3.
    Cai, X., Han, G.: Improved Statistical Image Fusion Method Using a Continuous-Valued Blur Factor. Opt. Eng. 51, 047004-1–047004-10 (2012)Google Scholar
  4. 4.
    Chandana, M., Amutha, S., Kumar, N.: A Hybrid Multi-focus Medical Image Fusion Based on Wavelet Transform. Int. J. Res. Rev. Comput. Sci. 2, 948–953 (2011)Google Scholar
  5. 5.
    Do, M.N., Vetterli, M.: The Contourlet Transform: An Efficient Directional Multiresolution Image Representation. IEEE Trans. Image Process. 14, 2091–2106 (2005)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Cai, X., Zhao, W.: Discussion upon Effects of Contourlet Lowpass Filter on Contourlet-Based Image Fusion Algorithms. Zidonghua Xuebao Acta Auto. Sin. 35, 258–266 (2009) (in Chinese)CrossRefGoogle Scholar
  7. 7.
    Miao, Q.G., Wang, B.S.: A Novel Image Fusion Method Using Contourlet Transform. In: 2006 IEEE International Conference on Communications, Circuits and Systems, pp. 548–552. IEEE Press, Guilin (2006)Google Scholar
  8. 8.
    Zheng, Y.A., Zhu, C.S., Song, J.S., et al.: Fusion of Multi-band SAR Images Based on Contourlet Transform. In: 2006 IEEE International Conference on Information Acquisition, pp. 420–424. IEEE Press, Weihai (2006)CrossRefGoogle Scholar
  9. 9.
    Wang, Z.B., Ma, Y.D., Cheng, F.Y., Yang, L.Z.: Review of Pulse-Coupled Neural Networks. Image Vision Comput. 28, 5–13 (2010)zbMATHCrossRefGoogle Scholar
  10. 10.
    Piella, G., Heijmans, H.: A New Quality Metric for Image Fusion. In: IEEE International Conference on Image Processing, pp. 173–176. IEEE Press, Barcelona (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xi Cai
    • 1
  • Guang Han
    • 2
  • Jinkuan Wang
    • 1
  1. 1.Northeastern University at QinhuangdaoQinhuangdaoChina
  2. 2.College of Information Science and EngineeringNortheastern UniversityShenyangChina

Personalised recommendations