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Laser & Optoelectronics Progress, Volume. 57, Issue 2, 21502(2020)

Augmented Reality Recognition Registration Method Based on Text Features

Li Xueting1,2、*, Dang Jianwu1,2, Wang Yangping1,2, and Gao Fanyi1,2
Author Affiliations
  • 1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
  • 2Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing, Lanzhou, Gansu 730070, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (17)
    Cited By (2)
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    Figures & Tables(13)
    AR recognition registration process based on text features

    Fig. 1. AR recognition registration process based on text features

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    FAST feature point detection

    Fig. 2. FAST feature point detection

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    FREAK retina sampling mode

    Fig. 3. FREAK retina sampling mode

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    Center symmetrical sampling point pair

    Fig. 4. Center symmetrical sampling point pair

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    Improved corner points of FASText

    Fig. 5. Improved corner points of FASText

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    Matching results of text images of class A. (a) SURF algorithm; (b) ORB algorithm;(c) FREAK algorithm; (d) improved FREAK algorithm

    Fig. 6. Matching results of text images of class A. (a) SURF algorithm; (b) ORB algorithm;(c) FREAK algorithm; (d) improved FREAK algorithm

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    Matching results of text images of class B. (a) SURF algorithm; (b) ORB algorithm;(c) FREAK algorithm; (d) improved FREAK algorithm

    Fig. 7. Matching results of text images of class B. (a) SURF algorithm; (b) ORB algorithm;(c) FREAK algorithm; (d) improved FREAK algorithm

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    Matching results of text images of class C. (a) SURF algorithm; (b) ORB algorithm;(c) FREAK algorithm; (d) improved FREAK algorithm

    Fig. 8. Matching results of text images of class C. (a) SURF algorithm; (b) ORB algorithm;(c) FREAK algorithm; (d) improved FREAK algorithm

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    Recognition registration for image A. (a) Positive registration; (b) distance and rotation change registration; (c) occlusion registration;(d) perspective change registration

    Fig. 9. Recognition registration for image A. (a) Positive registration; (b) distance and rotation change registration; (c) occlusion registration;(d) perspective change registration

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    Recognition registration for image B. (a) Positive registration; (b) distance and rotation change registration; (c) occlusion registration;(d) perspective change registration

    Fig. 10. Recognition registration for image B. (a) Positive registration; (b) distance and rotation change registration; (c) occlusion registration;(d) perspective change registration

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    Recognition registration for image C. (a) Positive registration; (b) distance and rotation change registration; (c) occlusion registration; (d) perspective change registration

    Fig. 11. Recognition registration for image C. (a) Positive registration; (b) distance and rotation change registration; (c) occlusion registration; (d) perspective change registration

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    • Table 1. Comparison of average running time, average number of feature point pairs, and matching precision of algorithms

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      Table 1. Comparison of average running time, average number of feature point pairs, and matching precision of algorithms

      ParameterSURFORBFREAKImproved FREAK
      ABCABCABCABC
      Average running time /ms36.655.257.634.538.239.333.836.537.631.832.733.1
      Contrast of averagerunning time /%-13.1-40.7-42.5-7.8-14.3-15.7-5.9-10.4-11.9---
      Average number offeature point pairs358572607336509535324469498302338342
      Contrast of average numberof feature point pairs /%-15.6-40.9-43.6-10.1-33.5-36.0-6.7-27.9-31.3---
      Precision /%69.664.262.376.975.574.678.776.275.487.885.484.6

    • Table 2. Average processing time of different algorithmsms

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      Table 2. Average processing time of different algorithmsms

      AlgorithmImageprocessingFeatureextractionFeaturematchingPoseestimationModelrenderingTotal time
      SURF1.321.618.18.76.456.1
      ORB1.318.217.59.86.453.2
      FREAK1.316.815.49.86.449.7
      Improved FREAK1.315.113.89.86.446.4
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    Li Xueting, Dang Jianwu, Wang Yangping, Gao Fanyi. Augmented Reality Recognition Registration Method Based on Text Features[J]. Laser & Optoelectronics Progress, 2020, 57(2): 21502

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    Paper Information

    Category: Machine Vision

    Received: May. 6, 2019

    Accepted: --

    Published Online: Jan. 3, 2020

    The Author Email: Li Xueting (LiXueting510@163.com)

    DOI:10.3788/LOP57.021502

    Topics

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