Performance Evaluation of Multiresolution Methods in Disparity Estimation

  • Dibyendu Mukherjee
  • Gaurav Bhatnagar
  • Q. M. Jonathan Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

Disparity estimation from stereo imagery has gained substantial interest of research community from its commencement with the recent trend being the use of multiresolution methods. Existing multiresolution based methods are relatively independent and do not, in general, relate to a continuous progress in the research. As a result, the relative advantages and disadvantages of a particular multiresolution method in disparity estimation are hard to understand. Present work is an effort to put different multiresolution methods together to highlight their expediency and suitability along with the comparison to get a better understanding. Three different frameworks are used having different strengths and limitations followed by the comparison in the terms of time complexity, quality of matching and effect of different levels of decomposition. Qualitative and quantitative results have been provided for four types of standard multiresolution methods.

Keywords

Stereo Match Disparity Estimation Detail Part Contourlet Transform Stereo Match Algorithm 
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.

References

  1. 1.
    Bhatti, A., Nahavandi, S.: Depth estimation using multiwavelet analysis based stereo vision approach. International Journal of Wavelets Multiresolution and Information Processing 6(3), 481–497 (2008)MATHCrossRefGoogle Scholar
  2. 2.
    Candes, E.J., Demanet, L., Donoho, D.L., Ying, L.: Fast discrete curvelet transforms. Multiscale Modelling and Simulation 5, 861–899 (2006)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Caspary, G., Zeevi, Y.Y.: Wavelet-based multiresolution stereo vision. In: Proceedings of the International Conference on Pattern Recognition, vol. 3, pp. 680–683 (2002)Google Scholar
  4. 4.
    Ding, H., Fu, M., Wang, M.: Shift-invariant contourlet transform and its application to stereo matching. In: Proceedings of the International Conference on Innovative Computing, Information and Control, pp. 87–90 (2006)Google Scholar
  5. 5.
    Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing 14(12), 2091–2106 (2005)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Kakarala, R., Ogunbona, P.: Signal analysis using a multiresolution form of the singular value decomposition. IEEE Transactions on Image Processing 10(5), 724–735 (2001)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Mallat, S.: Wavelets for a vision. Proceedings of IEEE 84(4), 604–614 (1996)CrossRefGoogle Scholar
  8. 8.
    Mukherjee, D., Wang, G., Wu, Q.: Stereo matching algorithm based on curvelet decomposition and modified support weights. Accepted at IEEE International Conference on Acoustics, Speech and Signal Processing (2010)Google Scholar
  9. 9.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47(1-3), 7–42 (2002)MATHCrossRefGoogle Scholar
  10. 10.
    Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 195–202 (2003)Google Scholar
  11. 11.
    Zhang, W., Zhang, Q., Qu, L., Wei, S.: A stereo matching algorithm based on multiresolution and epipolar constraint. In: Proceedings of the International Conference on Image and Graphics, pp. 180–183 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dibyendu Mukherjee
    • 1
  • Gaurav Bhatnagar
    • 1
  • Q. M. Jonathan Wu
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of WindsorWindsorCanada

Personalised recommendations