• Laser & Optoelectronics Progress
  • Vol. 57, Issue 12, 121008 (2020)
Bin Song, Ning Yan, Linlin Zhu, and Xiaodong Zhang*
Author Affiliations
  • State Key Laboratory of Precision Measuring Technology & Instruments, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
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    DOI: 10.3788/LOP57.121008 Cite this Article Set citation alerts
    Bin Song, Ning Yan, Linlin Zhu, Xiaodong Zhang. A Periodic Structural Parameter Inspection Method Based on Spectrum Characteristics[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121008 Copy Citation Text show less
    Typical periodic texture image and spectrum characteristic. (a) Fabric image; (b) 3D spectrum with a series of characteristic peaks
    Fig. 1. Typical periodic texture image and spectrum characteristic. (a) Fabric image; (b) 3D spectrum with a series of characteristic peaks
    Simulation texture image and spectrum characteristic. (a) Simulation texture image; (b) 3D spectrum; (c) transverse spectrum and characteristic peaks
    Fig. 2. Simulation texture image and spectrum characteristic. (a) Simulation texture image; (b) 3D spectrum; (c) transverse spectrum and characteristic peaks
    Simulation results of texture image with uniformity error and spectrum characteristic. (a) Simulation texture image; (b) 3D spectrum; (c) transverse spectrum characteristic peaks and peak diffusion
    Fig. 3. Simulation results of texture image with uniformity error and spectrum characteristic. (a) Simulation texture image; (b) 3D spectrum; (c) transverse spectrum characteristic peaks and peak diffusion
    Correlativity between inspected uniformity error and preseted uniformity error
    Fig. 4. Correlativity between inspected uniformity error and preseted uniformity error
    Analysis of the relationship between the texture inclination α and the rotation angle of spectrum characteristic point β. (a) Decomposition diagram of d; (b) spectrum characteristic analysis
    Fig. 5. Analysis of the relationship between the texture inclination α and the rotation angle of spectrum characteristic point β. (a) Decomposition diagram of d; (b) spectrum characteristic analysis
    User interface in the static density test software. (a) Texture image to be detected; (b) 2D spectrum and feature marker; (c) rotation-corrected image; (d) feature marker in the corrected image; (e) control of execution and output texture detection results
    Fig. 6. User interface in the static density test software. (a) Texture image to be detected; (b) 2D spectrum and feature marker; (c) rotation-corrected image; (d) feature marker in the corrected image; (e) control of execution and output texture detection results
    Histogram analysis of error of measurement results. (a)Data measured by proposed method; (b) data measured by the method in Ref. [20]
    Fig. 7. Histogram analysis of error of measurement results. (a)Data measured by proposed method; (b) data measured by the method in Ref. [20]
    Real-time acquisition and inspection system. (a) Structure of acquisition system; (b) display interface of inspection result
    Fig. 8. Real-time acquisition and inspection system. (a) Structure of acquisition system; (b) display interface of inspection result
    Fabric uniformity inspection results. (a)~(d) Fabric of uniform texture; (e)~(h) fabric stretched along transverse direction
    Fig. 9. Fabric uniformity inspection results. (a)~(d) Fabric of uniform texture; (e)~(h) fabric stretched along transverse direction
    Paper counts based on spectrum characteristics. (a) Cross-section image of paper; (b) preprocessing image; (c) 3D spectrum; (d) spectrum characteristics
    Fig. 10. Paper counts based on spectrum characteristics. (a) Cross-section image of paper; (b) preprocessing image; (c) 3D spectrum; (d) spectrum characteristics
    Wood texture inspection based on spectrum characteristics. (a) Wood texture image; (b) preprocessing image; (c) 3D spectrum; (d) spectrum characteristics
    Fig. 11. Wood texture inspection based on spectrum characteristics. (a) Wood texture image; (b) preprocessing image; (c) 3D spectrum; (d) spectrum characteristics
    Inspection of microstructural arrays based on spectrum characteristics. (a)Texture image of microstructural arrays; (b) preprocessing image; (c) 3D spectrum; (d) spectrum characteristics
    Fig. 12. Inspection of microstructural arrays based on spectrum characteristics. (a)Texture image of microstructural arrays; (b) preprocessing image; (c) 3D spectrum; (d) spectrum characteristics
    MethodsSplineCubicBilinearPolyfit
    Maximum error /%0.270.320.500.51
    Median error /%0.100.150.200.20
    Average error /%0.150.170.250.26
    Table 1. Statistical results of measurement errors by different optimization methods
    No.P1Error /%P2Error /%
    ManualAutomaticManualAutomatic
    186.185.91.399.699.20.6
    268.568.00.771.871.51.1
    379.680.00.587.588.20.6
    478.678.50.887.186.50.1
    581.381.10.488.588.20.6
    686.587.10.677.577.90.6
    787.486.90.584.384.00.4
    885.585.70.684.084.51.2
    967.567.80.763.664.00.6
    1083.383.50.473.973.41.2
    1184.984.71.179.078.41.3
    1284.685.00.575.876.00.3
    1378.679.00.581.682.10.5
    1469.169.81.370.470.00.6
    1575.876.20.382.482.20.5
    Table 2. Density measurement results by proposed method
    No.P1Error /%P2Error /%
    ManualAutomaticManualAutomatic
    135.034.61.1446.546.10.86
    242.041.51.1955.055.40.73
    382.583.00.6193.592.21.39
    455.055.40.7361.562.31.30
    559.059.91.5382.083.01.22
    658.057.70.5293.092.30.75
    744.543.81.5782.583.00.61
    857.057.40.7079.579.60.31
    959.560.00.8497.096.90.10
    1082.583.00.6188.087.70.34
    1160.059.90.17101.0101.50.50
    1259.559.20.5096.095.80.21
    Table 3. Density measurement results in Ref. [20]
    Bin Song, Ning Yan, Linlin Zhu, Xiaodong Zhang. A Periodic Structural Parameter Inspection Method Based on Spectrum Characteristics[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121008
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