Acta Optica Sinica, Volume. 38, Issue 4, 0415002(2018)

Feature Point Extraction and Matching Algorithm of Smooth Surfaces Without Chromatic Aberration

Pengfei Xu1, Zhaoliang Jiang1,2、*, Yang Zhao1, and Xianmeng Zhu1
Author Affiliations
  • 1 School of Mechanical Engineering, Shandong University, Jinan, Shandong 250061, China
  • 2 Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan, Shandong 250061, China
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    Figures & Tables(15)
    Layout of the surface to be measured, a laser and cameras
    Examples of image preprocessing. (a) Experimental surface after irradiating; (b) partial enlarged image after binaryzation; (c) partial enlarged image after de-noising; (d) partial enlarged image after thinning
    Partial enlarged images of different types of connected points. (a) Double angle type; (b) T and angle type; (c) point and angle type; (d) T and point type; (e) cross type; (f) overlapping type
    Partial enlarged images of connected points after filtration. (a) Double angle type; (b) T and angle type; (c) point and angle type; (d) T and point type; (e) cross type; (f) overlapping type
    Partial enlarged images of connected points after second filtration. (a) Double angle type; (b) T and angle type; (c) point and angle type; (d) T and point type; (e) cross type; (f) overlapping type
    Comparison of two feature point extraction algorithms
    Example of minimum distance distribution between feature points
    Example of matching result
    Extraction and matching results of left and right cameras. (a) Pot; (b) grid plate; (c) china cup
    Extraction results of feature points in different references. (a) Ref. [20]; (b) Ref. [14]; (c) Ref. [16]; (d) Ref. [17]
    Objects to be measured. (a) Grid plate plane; (b) white paper plane; (c) stripe paper plane
    • Table 1. Number of feature points and false feature points

      View table

      Table 1. Number of feature points and false feature points

      Experimental subjectCameraTE
      Grid plate surfaceLeft17590
      Right16391
      Pot surfaceLeft12243
      Right12300
      China cup surfaceLeft10260
      Right10162
    • Table 2. Distance error and standard deviation of feature pointsmm

      View table

      Table 2. Distance error and standard deviation of feature pointsmm

      AlgorithmDistance errorσ
      <0.05<0.1<0.2
      Proposed method0.96600.99210.99740.0362
      Algorithm in Ref. [20]0.50000.90000.9900
      Algorithm in Ref. [14]Average error is 0.10310.0501
      Algorithm in Ref. [16]Average error is 0.0750
      Algorithm in Ref. [17]Average error is 0.8362
    • Table 3. Matching accuracy rate and repetition rate

      View table

      Table 3. Matching accuracy rate and repetition rate

      Experimental subjectCameraβaβr
      Grid plate surfaceLeft1.00000.9227
      Right0.99930.9902
      Pot surfaceLeft0.99750.9501
      Right1.00000.9455
      China cup surfaceLeft1.00000.9512
      Right0.99800.9606
    • Table 4. Extraction and matching effects (left camera/right camera)

      View table

      Table 4. Extraction and matching effects (left camera/right camera)

      Test itemGrid plate surfaceWhite paper surfaceStripe paper surface
      10000 lx<0.05 lx10000 lx<0.05 lx10000 lx<0.05 lx
      T1842/17221756/16361918/19071849/18422031/19161910/1843
      βa1.00/1.001.00/1.001.00/1.001.00/1.001.00/1.001.00/1.00
      βr0.92/0.990.93/1.000.98/0.990.99/0.990.94/1.000.96/1.00
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    Pengfei Xu, Zhaoliang Jiang, Yang Zhao, Xianmeng Zhu. Feature Point Extraction and Matching Algorithm of Smooth Surfaces Without Chromatic Aberration[J]. Acta Optica Sinica, 2018, 38(4): 0415002

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    Paper Information

    Category: Machine Vision

    Received: Sep. 1, 2017

    Accepted: --

    Published Online: Jul. 10, 2018

    The Author Email: Jiang Zhaoliang (jiangzhaoliang@sdu.edu.cn)

    DOI:10.3788/AOS201838.0415002

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