Digital Image Stabilization Using a Functional Neural Fuzzy Network

  • Chi-Feng Wu
  • Cheng-Jian Lin
  • Yu-Jia Shiue
  • Chi-Yung Lee
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 144)


This study proposed a real-time video stabilization method to eliminate the unwanted shakes, preserve the intended panning of camera, and improve the stability of the captured video sequence. The proposed method uses a functional neuro-fuzzy network to learn the phenomena of different shakes and then it chooses adequate compensation weight for two different methods to calculate the compensated motion vector. Experimental results show that the proposed method has superior performance than other motion compensation methods.


Motion Vector Current Frame Motion Compensation Smoothness Index Video Stabilization 
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.
    Chen, C.H., Kuo, Y.L., Chen, T.Y., Chen, J.R.: Real-time video stabilization based on motion compensation. In: Fourth Inter. Conf. on Innovative Computing, Information and Control, pp. 1495–1498 (2009)Google Scholar
  2. 2.
    Hsu, S.C., Liang, S.F., Fan, K.W., Lin, C.T.: A robust in-car digital image stabilization technique. IEEE Trans. on System, Man, and Cyber. Part C: Applications and Reviews 37(2), 234–247 (2007)CrossRefGoogle Scholar
  3. 3.
    Wang, C., Kim, J.H., Byun, K.Y., Ni, J., Ko, S.J.: Robust digital image stabilization using kalman filter. IEEE Trans. on Consumer Electronics 55(1), 6–13 (2009)CrossRefGoogle Scholar
  4. 4.
    Cai, J., Walker, R.: Robust video stabilization algorithm using feature point selection and delta optical flow. IET Comput. Vis. 3(4), 176–188 (2009)CrossRefGoogle Scholar
  5. 5.
    Yang, J., Schonfeld, D., Mohamed, M.: Robust video stabilization based on particle filter tracking of projected camera motion. IEEE Trans. on Circuits and Systems for Video Technology 19(7) (July 2009)Google Scholar
  6. 6.
    Liang, Y.M., Tyan, H.R., Chang, S.L., Liao, H.Y.M., Chen, S.W.: Video stabilization for a camcorder mounted on a moving vehicle. IEEE Trans. on Vehicular Technology 53(6), 1636–1648 (2004)CrossRefGoogle Scholar
  7. 7.
    Paik, J.K., Park, Y.C., Kim, D.W.: An adaptive motion decision system for digital image stabilizer based on edge pattern matching. IEEE Trans. on Consum. Electron. 38(3), 607–616 (1992)CrossRefGoogle Scholar
  8. 8.
    Hsu, S.C., Lin, C.T.: Fuzzy inference applied to digital image stabilization techniques. Image and Recognition 13(3), 55–66 (2007)MathSciNetGoogle Scholar
  9. 9.
    Lin, C.J., Liu, Y.C., Lee, C.Y.: An efficient neural fuzzy network based on immune particle swarm optimization for prediction and control applications. Int. Journal of Innovative Computing, Information and Control 4(7), 1711–1721 (2008)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Chi-Feng Wu
    • 1
  • Cheng-Jian Lin
    • 2
  • Yu-Jia Shiue
    • 2
  • Chi-Yung Lee
    • 3
  1. 1.Department of Information Management and CommunicationWenzao Ursuline College of LanguagesKaohsiung CityTaiwan, R.O.C.
  2. 2.Department of Computer Science and Information EngineeringNational Chin-Yi University of TechnologyTaiping CityTaiwan, R.O.C.
  3. 3.Department of Computer Science and Information EngineeringNankai University of TechnologyNantouTaiwan R.O.C.

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