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Laser & Optoelectronics Progress, Volume. 58, Issue 14, 1433002(2021)

Stereo Matching Based on Improved Census Transformation and Adaptive Support Region

Yingguang Chen1, Pei Zhou1,2、*, Jiangping Zhu1,2, and Sancong Ying1,2
Author Affiliations
  • 1National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, Sichuan 610065, China
  • 2College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
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    References (24)
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    Figures & Tables(8)
    Comparison between traditional Census and improved Census

    Fig. 1. Comparison between traditional Census and improved Census

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    Schematic for construction of adaptive support region. (a) Vertical support arm; (b) adaptive support region

    Fig. 2. Schematic for construction of adaptive support region. (a) Vertical support arm; (b) adaptive support region

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    Comparison among different Census algorithms. (a) Traditional Census algorithm; (b) Census 1; (c) Census 2; (d) proposed Census algorithm

    Fig. 3. Comparison among different Census algorithms. (a) Traditional Census algorithm; (b) Census 1; (c) Census 2; (d) proposed Census algorithm

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    Mismatch rate comparison among different Census algorithms

    Fig. 4. Mismatch rate comparison among different Census algorithms

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    Verification of optimization effect. (a) Input images; (b) adaptive support regions; (c) disparity maps

    Fig. 5. Verification of optimization effect. (a) Input images; (b) adaptive support regions; (c) disparity maps

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    Comparison of results. (a) Reference images; (b) real disparity maps; (c) proposed algorithm; (d) CC-SGM; (e) VarCross; (f) GlobalGCP

    Fig. 6. Comparison of results. (a) Reference images; (b) real disparity maps; (c) proposed algorithm; (d) CC-SGM; (e) VarCross; (f) GlobalGCP

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    Matching results. (a) Reference images; (b) real disparity maps; (c) disparity maps obtained by proposed algorithm; (d) mismatched images

    Fig. 7. Matching results. (a) Reference images; (b) real disparity maps; (c) disparity maps obtained by proposed algorithm; (d) mismatched images

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    • Table 1. Mismatch rate comparison among proposed and other algorithms unit: %

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      Table 1. Mismatch rate comparison among proposed and other algorithms unit: %

      AlgorithmTsukubaVenusTeddyConesAvgPBM
      N-OccAllDiscN-OccAllDiscN-OccAllDiscN-OccAllDisc
      Proposed1.291.756.120.220.712.324.479.6912.283.009.078.074.92
      CS-GF2.382.858.401.131.989.267.0514.9016.803.3211.007.997.09
      Census-DP1.411.746.860.270.383.045.9411.4014.902.647.867.375.31
      GlobalGCP0.872.544.690.460.532.226.4411.5016.203.599.498.905.60
      VarCross1.992.656.770.620.963.209.7515.1018.206.2812.7012.907.60
      CC-SGM2.803.108.701.702.103.707.4011.8014.005.106.407.008.20
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    Yingguang Chen, Pei Zhou, Jiangping Zhu, Sancong Ying. Stereo Matching Based on Improved Census Transformation and Adaptive Support Region[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1433002

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    Paper Information

    Category: Vision, Color, and Visual Optics

    Received: Sep. 24, 2020

    Accepted: Dec. 2, 2020

    Published Online: Jul. 14, 2021

    The Author Email: Pei Zhou (michille78@163.com)

    DOI:10.3788/LOP202158.1433002

    Topics

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