• Acta Optica Sinica
  • Vol. 38, Issue 11, 1112005 (2018)
Zhenmin Zhu1、*, Shuang Pei1、*, Shiming Chen1, and Fumin Zhang2
Author Affiliations
  • 1 School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, Jiangxi 330013, China
  • 2 State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China
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    DOI: 10.3788/AOS201838.1112005 Cite this Article Set citation alerts
    Zhenmin Zhu, Shuang Pei, Shiming Chen, Fumin Zhang. Highlight Removal of High Reflectivity Workpiece and Vision Measurement Based on Polarization Information[J]. Acta Optica Sinica, 2018, 38(11): 1112005 Copy Citation Text show less
    p type polarization light
    Fig. 1. p type polarization light
    Image acquisition system for vision measurement based on polarization device
    Fig. 2. Image acquisition system for vision measurement based on polarization device
    Image acquisition on object surface under different polarization angles
    Fig. 3. Image acquisition on object surface under different polarization angles
    White balance processing of image and corresponding histogram features. (a) Color image; (b) histogram of color image; (c) white balanced image; (d) histogram of white balanced image
    Fig. 4. White balance processing of image and corresponding histogram features. (a) Color image; (b) histogram of color image; (c) white balanced image; (d) histogram of white balanced image
    Flowchart of mean-shift segmentation algorithm based on dark channel images
    Fig. 5. Flowchart of mean-shift segmentation algorithm based on dark channel images
    Images acquired under different polarization angles, image with highlight components and pixel luminance distribution in two-color reflection model
    Fig. 6. Images acquired under different polarization angles, image with highlight components and pixel luminance distribution in two-color reflection model
    BP neural network model for highlight removal based on polarization information
    Fig. 7. BP neural network model for highlight removal based on polarization information
    Flowchart of image highlight removal based on polarization information
    Fig. 8. Flowchart of image highlight removal based on polarization information
    Effect of image highlight removal based on polarization information. (a) Image acquired under optimal polarization angle; (b) pixel luminance distribution corresponding to (a); (c) image after highlight removal; (d) pixel luminance distribution corresponding to (c)
    Fig. 9. Effect of image highlight removal based on polarization information. (a) Image acquired under optimal polarization angle; (b) pixel luminance distribution corresponding to (a); (c) image after highlight removal; (d) pixel luminance distribution corresponding to (c)
    Detail comparison of SIFT feature extraction. (a) SIFT feature extraction of image under optimal polarization angle; (b) SIFT feature extraction of image under optimal polarization angle after treatment by RANSAC algorithm; (c) SIFT feature extraction of image after highlight removal; (d) SIFT feature extraction of image after highlight removal and treatment by RANSAC algorithm
    Fig. 10. Detail comparison of SIFT feature extraction. (a) SIFT feature extraction of image under optimal polarization angle; (b) SIFT feature extraction of image under optimal polarization angle after treatment by RANSAC algorithm; (c) SIFT feature extraction of image after highlight removal; (d) SIFT feature extraction of image after highlight removal and treatment by RANSAC algorithm
    Detail comparison of SURF feature extraction. (a) SURF feature extraction of image under optimal polarization angle; (b) SURF feature extraction of image under optimal polarization angle after treatment by RANSAC algorithm; (c) SURF feature extraction of image after highlight removal; (d) SURF feature extraction of image after highlight removal and treatment by RANSAC algorithm
    Fig. 11. Detail comparison of SURF feature extraction. (a) SURF feature extraction of image under optimal polarization angle; (b) SURF feature extraction of image under optimal polarization angle after treatment by RANSAC algorithm; (c) SURF feature extraction of image after highlight removal; (d) SURF feature extraction of image after highlight removal and treatment by RANSAC algorithm
    Boundary contours extracted under different conditions. (a) After highlight removal; (b) under optimal polarization angle
    Fig. 12. Boundary contours extracted under different conditions. (a) After highlight removal; (b) under optimal polarization angle
    Three-dimensional space coordinates of feature points of experimental object’s contour
    Fig. 13. Three-dimensional space coordinates of feature points of experimental object’s contour
    ConditionOptimal polarization angleImage highlight removal
    ILeftIRightILeftIRight
    SIFT7085150162
    SIFT matching7070150150
    SIFT matching after RANASC55558989
    SURF8293180205
    SURF matching8282180180
    SURF matching after RANASC6565165165
    Table 1. Number of feature points on workpiece surface under different conditions
    No.(x, y)(X, Y, Z)Space length /mm
    1(917.5, 719.5)(467.18, 1192.5, -18412)11.07
    2(747.5, 719.5)(456.12, 1192.5, -18412)11.28
    3(659.5, 567.5)(450.37, 1182.8, -18413)11.34
    4(747.5, 413.5)(456.08, 1173, -18414)11.07
    5(917.5, 411.5)(467.14, 1172.9, -18414)11.50
    6(1005.5, 567.5)(472.89, 1182.8, -18413)11.23
    Table 2. Coordinates of feature points for Sobel edge detection and corresponding world coordinates
    ConditionSide length /mmMeasurement accuracy /mm
    Optimal polarization11.250.75
    Image highlight removal10.091.91
    Table 3. Comparison of measurement accuracy based on image highlight removal
    Zhenmin Zhu, Shuang Pei, Shiming Chen, Fumin Zhang. Highlight Removal of High Reflectivity Workpiece and Vision Measurement Based on Polarization Information[J]. Acta Optica Sinica, 2018, 38(11): 1112005
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