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Laser & Optoelectronics Progress, Volume. 58, Issue 8, 0810025(2021)

Algorithm for Eliminating Mismatched Points Based on Pearson Correlation Coefficient

Shuo Li1,2, Yingdong Han1,2、*, Shuang Wang1,2, Kun Liu1,2, Junfeng Jiang1,2, and Tiegen Liu1,2
Author Affiliations
  • 1School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
  • 2Key Laboratory of Optoelectronics Information Technology, Ministry of Education, Tianjin University, Tianjin 300072, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
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    References (23)
    Cited By (3)
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    Figures & Tables(12)
    Relationship between size of Pearson correlation coefficient and law of scatter distribution

    Fig. 1. Relationship between size of Pearson correlation coefficient and law of scatter distribution

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    Using the length feature to detect mismatched points

    Fig. 2. Using the length feature to detect mismatched points

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    Using the length feature and the angle feature together to detect mismatched points

    Fig. 3. Using the length feature and the angle feature together to detect mismatched points

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    Flowchart of rough eliminating stage

    Fig. 4. Flowchart of rough eliminating stage

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    Scatter diagram of confidence and selection of inflection point

    Fig. 5. Scatter diagram of confidence and selection of inflection point

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    Method for selecting a threshold value of rough eliminating stage

    Fig. 6. Method for selecting a threshold value of rough eliminating stage

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    Flowchart of refined eliminating stage

    Fig. 7. Flowchart of refined eliminating stage

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    Results of contrast experiment 1. (a) Proposed algorithm; (b) MSAC algorithm; (c) PROSAC algorithm; (d) RANSAC algorithm

    Fig. 8. Results of contrast experiment 1. (a) Proposed algorithm; (b) MSAC algorithm; (c) PROSAC algorithm; (d) RANSAC algorithm

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    Results of contrast experiment 2. (a) Proposed algorithm; (b) MSAC algorithm; (c) PROSAC algorithm; (d) RANSAC algorithm

    Fig. 9. Results of contrast experiment 2. (a) Proposed algorithm; (b) MSAC algorithm; (c) PROSAC algorithm; (d) RANSAC algorithm

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    Results of contrast experiment 3. (a) Proposed algorithm; (b) MSAC algorithm; (c) PROSAC algorithm; (d) RANSAC algorithm

    Fig. 10. Results of contrast experiment 3. (a) Proposed algorithm; (b) MSAC algorithm; (c) PROSAC algorithm; (d) RANSAC algorithm

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    • Table 1. Accuracy of different algorithms for eliminating mismatched points

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      Table 1. Accuracy of different algorithms for eliminating mismatched points

      Contrast experimentAlgorithmNumber of eliminated matched point pairsNumber of mismatched point pairsAccuracy
      Experiment 1MSAC44210.477
      PROSAC460.457
      RANSAC520.404
      Proposed algorithm230.913
      Experiment 2MSAC74460.622
      PROSAC830.554
      RANSAC800.575
      Proposed algorithm480.958
      Experiment 3MSAC1731170.676
      PROSAC1670.701
      RANSAC2030.576
      Proposed algorithm1340.873

    • Table 2. Speed of different algorithms for eliminating mismatched points unit: ms

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      Table 2. Speed of different algorithms for eliminating mismatched points unit: ms

      Contrast experimentMSACPROSACRANSACProposed algorithm
      Experiment 1931.898322.995939.1272719.902
      Experiment 21130.817410.0441126.3215937.923
      Experiment 31073.104293.6291045.4816728.902
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    Shuo Li, Yingdong Han, Shuang Wang, Kun Liu, Junfeng Jiang, Tiegen Liu. Algorithm for Eliminating Mismatched Points Based on Pearson Correlation Coefficient[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810025

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    Paper Information

    Category: Image Processing

    Received: Sep. 24, 2020

    Accepted: Dec. 2, 2020

    Published Online: Apr. 12, 2021

    The Author Email: Han Yingdong (yingdong.han@tju.edu.cn)

    DOI:10.3788/LOP202158.0810025

    Topics

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