Advertisement

Segment-Based Stereo Matching Using Energy-Based Regularization

  • Dongbo Min
  • Sangun Yoon
  • Kwanghoon Sohn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)

Abstract

We propose a new stereo matching algorithm through energy-based regularization using color segmentation and visibility constraint. Plane parameters in the entire segments are modeled by robust least square algorithm, which is LMedS method. Then, plane parameter assignment is performed by the cost function penalized for occlusion, iteratively. Finally, disparity regularization which considers the smoothness between the segments and penalizes the occlusion through visibility constraint is performed. For occlusion and disparity estimation, we include the iterative optimization scheme in the energy-based regularization. Experimental results show that the proposed algorithm produces comparable performance to the state-of-the-arts especially in the object boundaries, un-textured regions.

Keywords

Plane Parameter Stereo Match Valid Point Color Segmentation Disparity Estimation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47, 7–42 (2002)MATHCrossRefGoogle Scholar
  2. 2.
    Boykov, Y., Veksler, O., Zabih, R.: Fast Approximate Energy Minimization via Graph Cuts. IEEE Trans. PAMI 23, 1222–1239 (2001)Google Scholar
  3. 3.
    Sun, J., Zheng, N.N., Shum, H.Y.: Stereo matching using belief propagation. IEEE Trans. PAMI 25, 787–800 (2003)Google Scholar
  4. 4.
    Tao, H., Sawhney, H.: A global matching framework for stereo computation. In: Proc. ICCV, pp. 532–539 (2001)Google Scholar
  5. 5.
    Hong, L., Chen, G.: Segment-based stereo matching using graph cuts. In: Proc. IEEE CVPR, pp. 74–81 (2004)Google Scholar
  6. 6.
    Bleyer, M., Gelautz, M.: A layered stereo algorithm using image segmentation and global visibility constraints. In: Proc. IEEE ICIP, pp. 2997–3000 (2004)Google Scholar
  7. 7.
    Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  8. 8.
    Alvarez, L., Deriche, R., Sanchez, J., Weickert, J.: Dense Disparity Map Estimation Respecting Image Discontinuities: A PDE and Scale-space Based Approach. J. of VCIR 13, 3–21 (2002)Google Scholar
  9. 9.
    Christoudias, C., Georgescu, B., Meer, P.: Synergism in low-level vision. In: Proc. IEEE ICPR, vol. 4, pp. 150–155 (2002)Google Scholar
  10. 10.
    Kim, H., Sohn, K.: Hierarchical disparity estimation with energy-based regularization. In: Proc. IEEE ICIP, vol. 1, pp. 373–376 (2003)Google Scholar
  11. 11.
    Zhang, Z., Deriche, R., Faugeras, O., Luong, Q.: A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence 78, 87–119 (1995)CrossRefGoogle Scholar
  12. 12.
    Strecha, C., Tuytelaars, T., Van Gool, L.: Dense matching of multiple wide-baseline views. In: Proc. ICCV, pp. 1194–1201 (2003)Google Scholar
  13. 13.
    Shao, J.: Generation of Temporally Consistent Multiple Virtual Camera Views from stereoscopic image sequences. IJCV 47, 171–180 (2002)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dongbo Min
    • 1
  • Sangun Yoon
    • 1
  • Kwanghoon Sohn
    • 1
  1. 1.Dept. of Electrical and Electronics Eng.Yonsei UniversitySeodaemun-gu, SeoulKorea

Personalised recommendations