LIU Jie, ZHANG Jianxun, DAI Yu. Dense Stereo Matching Based on Region Growing[J]. ROBOT, 2017, 39(2): 182-188. DOI: 10.13973/j.cnki.robot.2017.0182
Citation: LIU Jie, ZHANG Jianxun, DAI Yu. Dense Stereo Matching Based on Region Growing[J]. ROBOT, 2017, 39(2): 182-188. DOI: 10.13973/j.cnki.robot.2017.0182

Dense Stereo Matching Based on Region Growing

More Information
  • Received Date: October 20, 2016
  • Revised Date: March 09, 2017
  • Available Online: October 26, 2022
  • Published Date: March 19, 2017
  • For dense stereo matching, a local stereo matching algorithm based on region growing is proposed to solve the difficulties in choosing the size and shape of support windows as well as the pixels that have similar depth, and the difficulties in dealing with the boundaries of objects as well as the occlusion areas. Firstly, color similarity and connectivity constraint are adopted to obtain the adaptive support window dynamically by means of a method based on region growing. Then, the weights of pixels in support window or search window are assigned by different strategies, to reduce the effect of the pixels with different depths on the support window, and abandon the support windows that don't conform to the shape similarity rule. Finally, the matching cost is computed. The initial disparity is obtained by means of WTA (winner takes all) strategy. For the support windows, the frequencies of disparities of the pixels in accord with the confidence level standard are computed, in which the disparity with the highest frequency is taken as the optimal disparity. Moreover, the algorithm complexity is reduced through analyzing and optimizing the processes of support region computation, cost aggregation, and disparity search. The proposed algorithm is tested on Middlebury platform, and the result shows its excellent performance.
  • [1]
    Banno A, Ikeuchi K. Disparity map refinement and 3D surface smoothing via directed anisotropic diffusion[J]. Computer Vision and Image Understanding, 2011, 115(5):611-619.
    [2]
    Saygili G, van der Maaten L, Hendriks E A. Feature based stereo matching using graph cuts[C]//Proceedings of ASCI-IPA-SIKS Tracks. 2011:1-6.
    [3]
    Fusiello A, Roberto V, Trucco E. Efficient stereo with multiple windowing[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA:IEEE, 1997:858-863.
    [4]
    Kanade T, Okutomi M. A stereo matching algorithm with an adaptive window:Theory and experiment[J]. IEEE Transac-tions on Pattern Analysis and Machine Intelligence, 1994, 16(9):920-932.
    [5]
    Yoon K J, Kweon I S. Adaptive support-weight approach for correspondence search[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4):650-656.
    [6]
    卢迪,林雪.多种相似性测度结合的局部立体匹配算法[J].机器人,2016,38(1):1-7.

    Lu D, Lin X. A local stereo matching algorithm based on the combination of multiple similarity measures[J]. Robot, 2016, 38(1):1-7.
    [7]
    Gerrits M, Bekaert P. Local stereo matching with segmentation-based outlier rejection[C]//3rd Canadian Conference on Computer and Robot Vision. Piscataway, USA:IEEE, 2006.
    [8]
    Tombari F, Mattoccia S, Stefano L D. Segmentation-based adaptive support for accurate stereo correspondence[C]//Advances in Image and Video Technology, Second Pacific Rim Symposium. Berlin, Germany:Springer, 2007:427-438.
    [9]
    Zhang K, Lu J B, Lafruit G. Cross-based local stereo matching using orthogonal integral images[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2009, 19(7):1073-1079.
    [10]
    唐丽,吴成柯,刘侍刚,等.基于区域增长的立体像对稠密匹配算法[J].计算机学报,2004,27(7):936-943.

    Tang L, Wu C K, Liu S G, et al. Image dense stereo matching by technique of region growing[J]. Chinese Journal of Computers, 2004, 27(7):936-943.
    [11]
    胡汉平,朱明.基于种子点传播的快速立体匹配[J].光学精密工程,2015,23(3):887-894.

    Hu H P, Zhu M. Fast stereo matching based on seed pixel propagation[J]. Optics and Precision Engineering, 2015, 23(3):887-894.
    [12]
    Wang P, Wu F. A local stereo matching algorithm based on region growing[C]//Advances on Digital Television and Wireless Multimedia Communications. Berlin, Germany:Springer, 2012:459-464.
    [13]
    Yang Q X. Stereo matching using tree filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(4):834-846.
    [14]
    Kim S, Kang S J, Kim Y H. Real-time stereo matching using extended binary weighted aggregation[J]. Digital Signal Processing, 2016, 53(C):51-61.
    [15]
    Shi H, Zhu H, Wang J, et al. Segment-based adaptive window and multi-feature fusion for stereo matching[J]. Journal of Algorithms & Computational Technology, 2016, 10(1):3-11.
    [16]
    Scharstein D, Szeliski R. Middlebury stereo vision page[DB/OL].[2016-10-15]. http://vision.middlebury.edu/stereo/.
    [17]
    Lee Z, Juang J, Nguyen T Q. Local disparity estimation with three-moded cross census and advanced support weight[J]. IEEE Transactions on Multimedia, 2013, 15(8):1855-1864.
    [18]
    Mattoccia S, Giardino S, Gambini A. Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering[C]//9th Asian Conference on Computer Vision. Berlin, Germany:Springer, 2009:371-380.
    [19]
    祝世平,李政.基于改进梯度和自适应窗口的立体匹配算法[J].光学学报,2015,35(1):115-123.

    Zhu S P, Li Z. A stereo matching algorithm using improved gradient and adaptive window[J]. Acta Optica Sinica, 2015, 35(1):115-123.
    [20]
    Wang L, Yang R G. Global stereo matching leveraged by sparse ground control points[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA:IEEE, 2011:3033-3040.

Catalog

    Article views (79) PDF downloads (425) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return