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Acta Optica Sinica, Volume. 37, Issue 12, 1215007(2017)

Stereo Matching Algorithm Based on Color Weights and Tree Dynamic Programming

Jinxin Xu1, Qingwu Li1,2、*, Yan Liu1,2, and Yifei You1
Author Affiliations
  • 1 College of Internet of Things Engineering, Hohai University, Changzhou, Jiangsu 213022, China
  • 2 Changzhou Key Laboratory of Sensor Networks and Environmental Sensing, Changzhou, Jiangsu 213002, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (9)
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    References (19)
    Cited By (6)
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    Figures & Tables(9)
    Disparity maps computed by AD-Census and proposed algorithms. (a) Reference image; (b) real disparity map; (c) AD-Census; (d) proposed algorithm

    Fig. 1. Disparity maps computed by AD-Census and proposed algorithms. (a) Reference image; (b) real disparity map; (c) AD-Census; (d) proposed algorithm

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    Cost aggregation strategy based on color weights. (a) Cost aggregation process; (b) row (column) aggregation process

    Fig. 2. Cost aggregation strategy based on color weights. (a) Cost aggregation process; (b) row (column) aggregation process

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    (a) Reference images; (b)disparity maps of original cross-based aggregation; (c) disparity maps of proposed algorithm

    Fig. 3. (a) Reference images; (b)disparity maps of original cross-based aggregation; (c) disparity maps of proposed algorithm

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    Tree structures. (a) Horizontal tree; (b) vertical tree

    Fig. 4. Tree structures. (a) Horizontal tree; (b) vertical tree

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    Disparity maps computed by different algorithms. (a) Reference images; (b) WTA algorithm; (c) DP algorithm; (d) proposed algorithm

    Fig. 5. Disparity maps computed by different algorithms. (a) Reference images; (b) WTA algorithm; (c) DP algorithm; (d) proposed algorithm

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    Comparison of mismatching rates among three algorithms in non-occluded regions

    Fig. 6. Comparison of mismatching rates among three algorithms in non-occluded regions

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    Experimental results of the Middlebury benchmark images. (a) Reference images; (b) real disparity maps; (c) disparity maps obtained by proposed algorithm

    Fig. 7. Experimental results of the Middlebury benchmark images. (a) Reference images; (b) real disparity maps; (c) disparity maps obtained by proposed algorithm

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    Disparity maps obtained by five algorithms. (a) CrossTrees+SP algorithm; (b) CostFilter algorithm; (c) TwoStep algorithm; (d) AdaptAggrDP algorithm; (e) proposed algorithm

    Fig. 8. Disparity maps obtained by five algorithms. (a) CrossTrees+SP algorithm; (b) CostFilter algorithm; (c) TwoStep algorithm; (d) AdaptAggrDP algorithm; (e) proposed algorithm

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    • Table 1. Mismatching rates of five algorithms%

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      Table 1. Mismatching rates of five algorithms%

      AlgorithmTsukubaVenusTeddyConesAverage error (Nonocc)
      NonoccAllDiscNonoccAllDiscNonoccAllDiscNonoccAllDisc
      Proposed1.252.186.720.240.843.275.5410.5014.902.779.027.982.45
      CostFilter1.521.857.610.200.392.426.1611.8016.002.718.247.662.65
      CrossTrees+SP1.681.997.820.220.322.846.2311.7014.802.527.717.502.66
      TwoStep2.913.6813.30.270.452.637.4212.6018.004.0910.1010.303.67
      AdaptAggrDP1.573.508.271.532.6912.406.7914.3016.205.5313.2014.803.86
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    Jinxin Xu, Qingwu Li, Yan Liu, Yifei You. Stereo Matching Algorithm Based on Color Weights and Tree Dynamic Programming[J]. Acta Optica Sinica, 2017, 37(12): 1215007

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    Paper Information

    Category: Machine Vision

    Received: Jul. 10, 2017

    Accepted: --

    Published Online: Sep. 6, 2018

    The Author Email: Li Qingwu (li_qingwu@163.com)

    DOI:10.3788/AOS201737.1215007

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology