Efficient Stereo Matching by Local Linear Filtering and Improved Dynamic Programming

  • Fang Zhou
  • Jianhua Li
  • Shuping Zhao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)


In this paper, we present a dense stereo correspondence algorithm combining local linear filtering and improved dynamic programming (DP) algorithm, which maintains good performance in both accuracy and speed. Traditional DP, as we all know having most advantage in efficiency among global stereo approaches, suffers from typical streaking artifacts. Recently, an enhanced DP-based algorithm can reduce streak effects well by employing vertical consistency constraint between the scanlines, but with ambiguous matching at object boundaries. To tackle this problem, a cost-filtering framework is deployed as a very fast edge preserving filter in the improved DP optimization, without additional burden of computational complexity. The performance evaluation using the Middlebury benchmark datasets demonstrate that our method produces results comparable to those state-of-the-art algorithms but is much more efficient.


stereo correspondence global stereo improved DP cost-filtering framework 


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  1. 1.
    Mattoccia, S.: Stereo Vision and Applications,
  2. 2.
    Scharstein, D., Szeliski, R.: Middlebury stereo evaluation - version 2,
  3. 3.
    Yoon, K.-J., Kweon, I.-S.: Adaptive Support-Weight Approach for Correspondence Search. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 650–656 (2006)CrossRefGoogle Scholar
  4. 4.
    Xu, Z., Ma, L., Kimachi, M., Suwa, M.: Efficient contrast invariant stereo correspondence using dynamic programming with vertical constraint. The Visual Computer 24(1), 45–55 (2008)zbMATHCrossRefGoogle Scholar
  5. 5.
    De-Maeztu, L., Mattoccia, S., Villanueva, A., Cabeza, R.: Linear stereo matching. In: ICCV, pp. 1708–1715 (2011)Google Scholar
  6. 6.
    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)zbMATHCrossRefGoogle Scholar
  7. 7.
    Bobick, A.F., Intille, S.S.: Large Occlusion Stereo. International Journal of Computer Vision 33(3), 181–200 (1999)CrossRefGoogle Scholar
  8. 8.
    He, K., Sun, J., Tang, X.: Guided Image Filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Belief Propagation for Early Vision. In: CVPR (1), pp. 261–268 (2004)Google Scholar
  10. 10.
    Bleyer, M., Rhemann, C., Gelautz, M.: Segmentation-Based Motion with Occlusions Using Graph-Cut Optimization. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 465–474. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Yang, Q., Wang, L., Yang, R., Wang, S., Liao, M., Nistr, D.: Real-time Global Stereo Matching Using Hierarchical Belief Propagation. In: BMVC, pp. 989–998 (2006)Google Scholar
  12. 12.
    Rhemann, C., Hosni, A., Bleyer, M., Rother, C., Gelautz, M.: Fast cost-volume filtering for visual correspondence and beyond. In: CVPR, pp. 3017–3024 (2011)Google Scholar
  13. 13.
    Gong, M., Yang, Y.-H.: Near Real-Time Reliable Stereo Matching Using Programmable Graphics Hardware. In: CVPR (1), pp. 924–931 (2005)Google Scholar
  14. 14.
    Kim, J.-C., Lee, K.M., Choi, B.-T., Lee, S.U.: A Dense Stereo Matching Using Two-Pass Dynamic Programming with Generalized Ground Control Points. In: CVPR (2), pp. 1075–1082 (2005)Google Scholar
  15. 15.
    Wang, L., Liao, M., Gong, M., Yang, R., Nistér, D.: High-Quality Real-Time Stereo Using Adaptive Cost Aggregation and Dynamic Programming. In: 3DPVT, pp. 798–805 (2006)Google Scholar
  16. 16.
    Mattoccia, S., Tombari, F., Di Stefano, L.: Stereo Vision Enabling Precise Border Localization Within a Scanline Optimization Framework. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 517–527. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Hirschmüller, H., Scharstein, D.: Evaluation of Stereo Matching Costs on Images with Radiometric Differences. IEEE Trans. Pattern Anal. Mach. Intell. 31(9), 1582–1599 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fang Zhou
    • 1
  • Jianhua Li
    • 1
  • Shuping Zhao
    • 2
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.Dalian Ocean UniversityDalianChina

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