Segment-Based Stereo Correspondence of Face Images Using Wavelets

  • C. J. Prabhakar
  • K. Jyothi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)


In this paper, we introduce color segmentation based stereo correspondence for face images using wavelets. The intensity based correlation techniques are commonly employed to estimate the similarities between the stereo image pair, sensitive to shift variations and relatively lower performance in the featureless regions. Therefore, instead of pixel intensity, we consider wavelet coefficients of an approximation band, which is less sensitive to the shift variation. The approximation subband of reference image is segmented using mean shift segmentation method. A self-adapting dissimilarity measure that combines sum of absolute differences of wavelet coefficients and a gradient is employed to generate a disparity map of the stereo pairs. In our method instead of assigning a disparity value to a pixel, a disparity plane is assigned to each segment. Results show that the proposed technique produces smoother disparity maps with less computation cost.


Stereo matching Discrete wavelet transform Disparity plane Segmentation 


  1. 1.
    Muhlmann K, Maier D, Hesser R, Manner R (2001) Calculating dense disparity maps from color stereo images, an efficient implementation. In: Proceedings of the IEEE workshop on stereo and multi-baseline vision (SMBV 2001), pp 30–36Google Scholar
  2. 2.
    Di Stefano L, Marchionni M, Mattoccia S, Neri G (2004) A fast area-based stereo matching algorithm. Image Vis Comput 22:983–1005Google Scholar
  3. 3.
    Yoon KJ, Kweon IS (2006) Adaptive support-weight approach for correspondence search. IEEE Trans Pattern Anal Mach Intell 28:650–656Google Scholar
  4. 4.
    Seok Y, Lee S (2011) Robust stereo matching using adaptive normalized cross correlation. IEEE Trans PAMI 33(4):807–822 Google Scholar
  5. 5.
    Hamzah R, Hamid A, Md. Salim S (2010) The solution of stereo correspondence problem using block matching algorithm in stereo vision mobile robot. IICRD, pp 733–737 Google Scholar
  6. 6.
    Bobick AF, Intille SS (1999) Large occlusion stereo. Int J Comput Vis 33(3):181–200CrossRefGoogle Scholar
  7. 7.
    Kang SB, Szeliski R, Jinxjang C (2001) Handling occlusions in dense multi-view stereo. Proceedings of the IEEE conference computer vision and pattern recognition, vol 1, pp 103–110Google Scholar
  8. 8.
    Kim H, Yang S, Sohn K (2003) 3D reconstruction of stereo images for interaction between real and virtual worlds. In: Proceedings of the IEEE international conference on mixed and augmented realityGoogle Scholar
  9. 9.
    Ogale AS, Aloimonos Y (2008) Robust contrast invariant stereo correspondence. Proceedings of the IEEE international conference on robotics and automation, ICRA 2005, pp 819–824Google Scholar
  10. 10.
    Bleyer M, Gelautz M (2005) A layered stereo matching algorithm using image segmentation and global visibility constraints. ISPRS J Photogramm Remote Sens 59(3):128–150Google Scholar
  11. 11.
    Bleyer M, Gelautz M (2005) Graph-based surface reconstruction from stereo pairs using image segmentation. In: SPIE, vol 5665, pp 288–299Google Scholar
  12. 12.
    Deng Y, Yang Q, Lin X, Tang X (2005) A symmetric patch-based correspondence model for occlusion handling. In: ICCV, pp II:1316–1322Google Scholar
  13. 13.
    Hong L, Chen G (2004) Segment-based stereo matching using graph cuts. In: CVPR, vol I, pp 74–81Google Scholar
  14. 14.
    Xiao J, Xia L, Lin L (2010) A segment based stereo matching method with ground control points. In: IEEE transaction on ESIATGoogle Scholar
  15. 15.
    Mallat S (1999) A wavelet tour of signal processing. Academic Press, New York Google Scholar
  16. 16.
    Sarkar I, Bansal M (2007) A wavelet-based multiresolution approach to solve the stereo correspondence problem using mutual information. IEEE Trans Syst Man Cybern 37:1009–1014Google Scholar
  17. 17.
    Begheri P, Sedan CV (2010) Stereo correspondence matching using multiwavelets. In: Fifth international conference on digital telecommunicationGoogle Scholar
  18. 18.
    Bhatti A, nahavandi S, Hossny M (2010) Wavelets/Multiwavelets bases and correspondence estimation problem; an analytic study. In: 11th international conference on control, automation, robotics and visionGoogle Scholar
  19. 19.
    Klaus A, Sormann M, Karner K (2006) Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: Proceeding of the ICPRGoogle Scholar
  20. 20.
    Comaniciu D, Meer P (2002) Mean shift a robust approach toward feature space analysis. IEEE PAMI 24:603–619Google Scholar
  21. 21.
    Fusiello A, Irsara L (2008) Quasi-euclidean uncalibrated epipolar rectification. In: ICPR, pp 1–4Google Scholar
  22. 22.
  23. 23.
    Middlebury database (2010)

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Studies in Computer ScienceKuvempu UniversityShankaraghattaIndia
  2. 2.Department of IS&EJ.N.N College of EngineeringShimogaIndia

Personalised recommendations