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    This study introduces a novel minimum-entropy analysis for line segment detection using Hough transform. The method accurately extracts line parameters like angle, length, and midpoint, even with image noise.

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    Area of Science:

    • Image Processing
    • Computer Vision
    • Pattern Recognition

    Background:

    • The Hough transform is a standard technique for detecting lines in images.
    • Extracting detailed line segment parameters beyond angle and distance can be challenging.

    Purpose of the Study:

    • To propose a new method for extracting comprehensive line segment parameters using minimum-entropy analysis.
    • To enhance the accuracy and robustness of line segment detection.

    Main Methods:

    • Utilizing minimum-entropy analysis on voting distributions in Hough space.
    • Computing entropies and statistical means for each column around Hough peaks.
    • Fitting quadratic and linear curves to voting entropies and means, respectively.

    Main Results:

    • Simultaneously computed line segment normal angle and length via quadratic fitting.
    • Computed line segment midpoint and normal distance via linear fitting and interpolation.
    • Demonstrated high detection accuracy on simulated and real-world images.

    Conclusions:

    • The proposed minimum-entropy method accurately extracts line segment parameters.
    • The method is robust against quantization error, background noise, and pixel disturbances.
    • Offers an improved approach for detailed line segment analysis in image processing.