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Automated defect identification from carrier fringe patterns using Wigner-Ville distribution and a machine

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    This summary is machine-generated.

    This study introduces a new automated method for identifying defects in fringe patterns using Wigner-Ville distribution and machine learning. The approach offers robust defect detection, overcoming limitations of traditional thresholding techniques.

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    Area of Science:

    • Optical Metrology
    • Machine Learning
    • Image Processing

    Background:

    • Automated defect identification in fringe patterns is crucial for quality control.
    • Traditional thresholding methods for defect detection are sensitive to parameter variations and noise.
    • Robust and adaptable defect detection techniques are needed for complex fringe patterns.

    Purpose of the Study:

    • To develop and validate a novel automated method for defect identification in fringe patterns.
    • To leverage Wigner-Ville distribution and supervised machine learning for enhanced defect detection.
    • To overcome the limitations of conventional thresholding-based defect detection techniques.

    Main Methods:

    • The proposed method utilizes the Wigner-Ville distribution of fringe signals.
    • A supervised machine learning algorithm is employed for defect classification.
    • Numerical simulations and experimental validation were conducted to assess the method's performance.

    Main Results:

    • The Wigner-Ville distribution combined with machine learning demonstrated robust detection of various fringe pattern defects.
    • The method effectively identified different defect types across various noise levels.
    • The approach showed practical applicability and outperformed thresholding-based techniques.

    Conclusions:

    • The developed automated method provides a robust solution for fringe pattern defect identification.
    • This machine learning-based approach offers improved accuracy and reliability compared to traditional methods.
    • The technique is suitable for real-world applications requiring precise defect analysis.