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Laser & Optoelectronics Progress, Volume. 55, Issue 5, 051007(2018)

Feature Point Matching Algorithm for Fusion of Color Invariants and SURB Detection

Shu Chen, Tian Yang*; , and Shunyuan Sun
Author Affiliations
  • School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
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    References (16)
    Cited By (2)
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    Figures & Tables(8)
    FAST corner detection

    Fig. 1. FAST corner detection

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    Matching effects of ORB algorithm under different illuminations and scales. (a)(b) Input images under different illuminations; (c) matching effect under illumination changes; (d)(e) input images under different scales; (f) matching effect under scale changes

    Fig. 2. Matching effects of ORB algorithm under different illuminations and scales. (a)(b) Input images under different illuminations; (c) matching effect under illumination changes; (d)(e) input images under different scales; (f) matching effect under scale changes

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    Process of proposed matching algorithm

    Fig. 3. Process of proposed matching algorithm

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    Image matching effect under different illuminations. (a) Input image; (b) test image 1; (c) test image 2; (d) test image 3; (e) match image 1; (f) match image 2; (g) match image 3

    Fig. 4. Image matching effect under different illuminations. (a) Input image; (b) test image 1; (c) test image 2; (d) test image 3; (e) match image 1; (f) match image 2; (g) match image 3

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    Image matching effect under different angles. (a) Input image; (b) test image 1; (c) test image 2; (d) test image 3; (e) match image 1; (f) match image 2; (g) match image 3

    Fig. 5. Image matching effect under different angles. (a) Input image; (b) test image 1; (c) test image 2; (d) test image 3; (e) match image 1; (f) match image 2; (g) match image 3

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    Image matching effect under different scales. (a) ORB algorithm; (b) algorithm in Ref. [13]; (c) proposed algorithm

    Fig. 6. Image matching effect under different scales. (a) ORB algorithm; (b) algorithm in Ref. [13]; (c) proposed algorithm

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    • Table 1. Comparison of mis-matching rates of each algorithm under different illuminations

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      Table 1. Comparison of mis-matching rates of each algorithm under different illuminations

      DataORB+RANSACORB+optimizedRANSACCORBAlgorithm inRef. [13]Proposed method
      T /sR /%T /sR /%T /sR /%T /sR /%T /sR /%
      Data 10.4111.420.325.560.468.5610.524.210.454.15
      Data 20.4321.580.3111.530.4711.7310.4911.380.448.65
      Data 30.4258.400.3422.860.4718.8610.5314.070.4510.38

    • Table 2. Comparison of mis-matching rates of each algorithm under different rotating angles

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      Table 2. Comparison of mis-matching rates of each algorithm under different rotating angles

      DataORB+RANSACORB+optimizedRANSACCORBAlgorithm in Ref. [13]Proposed method
      T /sR /%T /sR /%T /sR /%T /sR /%T /sR /%
      Data 10.4111.520.283.160.468.3610.496.580.442.50
      Data 20.3221.350.1918.740.354.959.464.210.343.48
      Data 30.3458.790.1910.860.398.709.685.680.365.31
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    Shu Chen, Tian Yang, Shunyuan Sun. Feature Point Matching Algorithm for Fusion of Color Invariants and SURB Detection[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051007

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    Paper Information

    Category: Image processing

    Received: Sep. 25, 2017

    Accepted: --

    Published Online: Sep. 11, 2018

    The Author Email: Tian Yang ( 1362244724@qq.com)

    DOI:10.3788/LOP55.051007

    Topics

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