Signal, Image and Video Processing

, Volume 9, Issue 4, pp 893–901 | Cite as

Anchor-diagonal-based shape adaptive local support region for efficient stereo matching

  • U. RaghavendraEmail author
  • Krishnamoorthi Makkithaya
  • A. K. Karunakar
Original Paper


Local stereo algorithms are preferred for real-time applications due to their computational efficiency. Deciding the size of the required local support region is a challenging task. It fails to estimate accurate disparity for small support region and introduces fattening effect for big support region. Hence, a shape adaptive local support region is necessary to achieve accurate disparity. This paper proposes an anchor-diagonal-based shape adaptive support region construction for stereo matching. The proposed algorithm dynamically constructs local support region, and the aggregated matching cost is used for Normalized Cross-Correlation-based similarity measure. The algorithm is evaluated using benchmarked Middlebury stereo evaluation, and the obtained disparities are efficient as compared to state-of-the-art methods.


Stereo matching Disparity estimation  Local support region 



This research is carried out under the scheme Structural PhD of Manipal University. Authors would like to thank Martin Humenberger from Austrian Institute of Technology, Vienna, Austria, for providing real-time stereo images.


  1. 1.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. J. Comput. Vis. 47(1), 7–42 (2002)CrossRefzbMATHGoogle Scholar
  2. 2.
    Fusiello, A., Roberto, V., Trucco, E.: Efficient stereo with multiple windowing. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 858–863. San Juan, Puerto Rico (1997)Google Scholar
  3. 3.
    Kang, S.B., Szeliski, R., Chai, J.: Handling occlusions in dense multi-view stereo. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 103–110. Kauai Hawaii (2001)Google Scholar
  4. 4.
    Veksler, O.: Stereo correspondence with compact windows via minimum ratio cycle. IEEE Trans. PAMI. 24(12), 1654–1660 (2002)CrossRefGoogle Scholar
  5. 5.
    Veksler, O.: Fast variable window for stereo correspondence using integral images. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 556–561. Wisconsin (2003)Google Scholar
  6. 6.
    Lu, J., Lafruit, G., Catthoor, F.: Anisotropic local high-confidence voting for accurate stereo correspondence. In: Proceedings SPIE-IST Electronic, Imaging, vol. 6812, pp. 605822-1–605822-10 (2008)Google Scholar
  7. 7.
    Xu, Y., Wang, D., Feng, T., Shum, H.Y.: Stereo computation using radial adaptive windows. In: Proceedings IEEE Conference on Pattern Recognition, vol. 3, pp. 595–598. Canada (2002)Google Scholar
  8. 8.
    Yoon, K.J., Kweon, S.: Adaptive support-weight approach for correspondence search. IEEE Trans. PAMI 28(4), 650–656 (2006)Google Scholar
  9. 9.
    Tombari, F., Mattoccia, S., Stefano, L.D.: Segmentation based adaptive support for accurate stereo correspondence. In: Proceedings of PSIVT, pp. 427–438. Springer, LNCS-4872, Santiago, Chile (2007)Google Scholar
  10. 10.
    Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. IEEE Trans. Circuits Syst. Video Technol. 19(7), 1073–1079 (2009)CrossRefGoogle Scholar
  11. 11.
    Kim, H.S., Yoo, J.M. Park, M.S., Dinh, T.N., Lee, G.S.: An anisotropic diffusion based on diagonal edges. In: IEEE International Conference on Advanced Communication Technology, pp. 384–388. Japan (2007)Google Scholar
  12. 12.
    Edirisinghe, E.A., Bedi, S.: Gradient-based predictor for diagonal edge pixels in JPEG-LS. IEEE Electron. Lett. 37(22), 1327–1328 (2001)CrossRefGoogle Scholar
  13. 13.
    Hirschmuller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. PAMI 31(9), 1582–1599 (2009)CrossRefGoogle Scholar
  14. 14.
    Heo, Y.S., Lee, K.M., Lee, S.U.: Robust stereo matching using adaptive normalized cross-correlation. IEEE Trans. PAMI 33(4), 807–822 (2011)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Scharstein, D., Szeliski, R.: Middlebury Stereo Vision Page (2008). [Online Accessed: March 2012]. Available:
  16. 16.
    Gu, Z., Su, X., Liu, Y., Zhang, Q.: Local stereo matching with adaptive support-weight, rank transform and disparity calibration. Pattern Recognit. Lett. 29(3), 1230–1235 (2008)CrossRefGoogle Scholar
  17. 17.
    Wang, L., Liao, M., Gong, M., Yang, R., Nister, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In: Proceedings 3D Data Processing, Visualization, and Transmission, pp. 798–805. North Corolina (2006)Google Scholar
  18. 18.
    Nalpantidis, L., Gasteratos, A.: Biologically and psychophysically inspired adaptive support weights algorithm for stereo correspondence. Robotics Auton. Syst. 58(5), 457–464 (2010)CrossRefGoogle Scholar
  19. 19.
    El-Etriby, S., Al-Hamadi, A., Michaelis, B.: Dense stereo correspondence with slanted surface using phase-based algorithm. In: IEEE International Symposium on Industrial Electronics, pp. 1807–1813. Vigo Spain (2007) Google Scholar
  20. 20.
    El-Etriby, S., Al-Hamadi, A., Michaelis, B.: Dense depth map reconstruction by phase difference-based algorithm under influence of perspective distortion. Int. J. Mach. Graph. Vis. 15(3), 349–361 (2006)Google Scholar
  21. 21.
    Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Trans. PAMI 30(6), 1068–1080 (2008)CrossRefGoogle Scholar
  22. 22.
    Humenberger, M., Zinner, C., Weber, M., Kubinger, W., Vincze, M.: A fast stereo matching algorithm suitable for embedded real-time systems. Comput. Vis. Image Underst. 114(11), 1180–1202 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • U. Raghavendra
    • 1
    Email author
  • Krishnamoorthi Makkithaya
    • 2
  • A. K. Karunakar
    • 1
  1. 1.Department of Computer Applications, Manipal Institute of TechnologyManipal UniversityManipalIndia
  2. 2.Department of Computer Science and Engineering, Manipal Institute of TechnologyManipal UniversityManipalIndia

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