Journal of Electrical Engineering & Technology

, Volume 14, Issue 1, pp 463–469 | Cite as

Stereo Matching with Confidence-Region Decomposition and Processing

  • Young Ju Jeong
  • C.-C. Jay KuoEmail author
Original Article


We introduce a new stereo matching algorithm that estimates disparities in high-confidence and low-confidence regions separately . Stereo matching algorithm play an important role in 3D rendering since 3D structures and virtual scenes can be built by disparity map. A complementary tree structure is adopted to identify the high-confidence region and estimate its disparity map using dynamic programming. Then, a disparity fitting algorithm restores the disparities in low-confidence regions using the color and disparity information of high-confidence regions through a global optimization technique. The proposed stereo matching algorithm enhances disparity values in both occlusion and difficult-to-estimate areas (e.g., thin objects), to yield a high quality disparity map.


Disparity estimation Stereo images Multiview synthesis 



This Research was supported by Sookmyung Women’s University Research Grants (1-1703-2051).


  1. 1.
    Dodgson NA (2005) Autostereoscopic 3d displays. Computer 38(8):31–36CrossRefGoogle Scholar
  2. 2.
    Lee G, Yoo J (2015) Disparity refinement near the object boundaries for virtual-view quality enhancement. J Electr Eng Technol 10(5):2189–2196CrossRefGoogle Scholar
  3. 3.
    Lee G, Yoo J (2014) Real-time virtual-viewpoint image synthesis algorithm using Kinect camera. J Electr Eng Technol 9(3):1016–1022CrossRefGoogle Scholar
  4. 4.
    Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47(1–3):7–42CrossRefzbMATHGoogle Scholar
  5. 5.
    Shah J (1993) A nonlinear diffusion model for discontinuous disparity and half-occlusions in stereo. In: Computer vision and pattern recognition, pp 34–40Google Scholar
  6. 6.
    Hong H-K, Ko MS, Seo Y-H, Ki D-W, Yoo J (2012) 3D conversion of 2D videoencoded by H.264. J Electr Eng Technol 7(6):990–1000CrossRefGoogle Scholar
  7. 7.
    Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239CrossRefGoogle Scholar
  8. 8.
    Veksler O (1999) Efficient graph-based energy minimization methods in computer vision. Ph.D. dissertation, Cornell UniversityGoogle Scholar
  9. 9.
    Felzenszwalb PF, Huttenlocher DP (2006) Efficient belief propagation for early vision. Int J Comput Vis 70(1):41–54CrossRefGoogle Scholar
  10. 10.
    Sun J, Zheng N-N, Shum H-Y (2003) Stereo matching using belief propagation. IEEE Trans Pattern Anal Mach Intell 25(7):787–800CrossRefzbMATHGoogle Scholar
  11. 11.
    Yang Q, Wang L, Ahuja N (2010) A constant-space belief propagation algorithm for stereo matching. In: Computer vision and pattern recognition pp. 1458–1465Google Scholar
  12. 12.
    Criminisi A, Blake A, Rother C, Shotton J, Torr PH (2007) Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming. Int J Comput Vis 71(1):89–110CrossRefGoogle Scholar
  13. 13.
    Cox IJ, Hingorani SL, Rao SB, Maggs BM (1996) A maximum likelihood stereo algorithm. Comput Vis Image Underst 63(3):542–567CrossRefGoogle Scholar
  14. 14.
    Li SZ (2009) Markov random field modeling in image analysis. Springer, New YorkzbMATHGoogle Scholar
  15. 15.
    Veksler O (2005) Stereo correspondence by dynamic programming on a tree. Comput Vis Pattern Recognit 2:384–390Google Scholar
  16. 16.
    Hirschmuller H (2005) Accurate and efficient stereo processing by semi-global matching and mutual information. Comput Vis Pattern Recognit 2:807–814Google Scholar
  17. 17.
    Kolmogorov V, Zabih R (2001) Computing visual correspondence with occlusions using graph cuts. In: IEEE international conference on computer vision, vol 2. IEEE, 2001, pp 508–515Google Scholar
  18. 18.
    Sun J, Li Y, Kang SB, Shum H-Y (2005) Symmetric stereo matching for occlusion handling. Comput Vis Pattern Recognit 2:399–406Google Scholar
  19. 19.
    Jeong YJ, Kim J, Lee HY, Park D (2013) Confidence stereo matching using complementary tree structures and global depth-color fitting. In: Proceedings IEEE international conference on consumer electroronics, pp 468–469Google Scholar
  20. 20.
    Bleyer M, Gelautz M (2008) Simple but effective tree structures for dynamic programming-based stereo matching. In: VISAPP, pp 415–422Google Scholar
  21. 21.
    Levin A, Lischinski D, Weiss Y (2008) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30(2):228–242CrossRefGoogle Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  1. 1.Ming-Hsieh Department of Electrical EngineeringUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of SoftwareSookmyung Women’s UniversitySeoulSouth Korea

Personalised recommendations