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Optical fiber geometry by gray-scale analysis with robust regression.

L Mamileti, C M Wang, M Young

    Applied Optics
    |August 21, 2010
    PubMed
    Summary

    Least-median-of-squares (LMS) regression robustly analyzes optical fiber end images, outperforming standard methods on damaged fibers. This robust regression technique effectively handles outlying data points for improved accuracy in optical fiber analysis.

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    Area of Science:

    • Optical Engineering
    • Image Analysis
    • Statistical Modeling

    Background:

    • Accurate analysis of optical fiber ends is crucial for telecommunications and sensing.
    • Traditional regression methods can be sensitive to noise and defects in image data.
    • Robust regression techniques offer potential improvements in analyzing imperfect datasets.

    Purpose of the Study:

    • To evaluate the efficacy of least-median-of-squares (LMS) regression for analyzing optical fiber end-face images.
    • To compare the performance of LMS regression against least-sum-of-squares (LSS) regression.
    • To assess the robustness of LMS regression in the presence of data outliers, such as those found in damaged fiber ends.

    Main Methods:

    • Application of least-median-of-squares (LMS) regression to analyze gray-scale images of optical fiber ends.

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  • Fitting ellipses to fiber end images using both LMS and least-sum-of-squares (LSS) regression.
  • Direct comparison of regression results on both pristine and intentionally damaged fiber end images.

    • Main Results:

    • LMS and LSS regression produced comparable results on a pristine fiber end.
    • LMS regression demonstrated significantly superior performance in analyzing a damaged fiber end.
    • Effective handling of outlying data points by LMS regression without explicit filtering was observed.

    Conclusions:

    • Least-median-of-squares (LMS) regression is a highly effective and robust method for analyzing optical fiber end-face images, especially those with defects.
    • LMS regression offers a significant advantage over traditional least-sum-of-squares methods when dealing with noise and outliers in optical fiber image analysis.
    • The findings suggest that LMS regression can improve the reliability and accuracy of optical fiber inspection and characterization.