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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Radically irregular data points (RIDPs) degrade ellipse fitting performance.
    • Existing methods struggle with data noise and ambiguity.

    Purpose of the Study:

    • Develop a robust ellipse fitting method resilient to RIDPs.
    • Address both single and coupled ellipse fitting challenges.

    Main Methods:

    • Utilized the maximum correntropy criterion with variable center (MCC-VC) and an adaptable Laplacian kernel.
    • Formulated and solved convex approximations for kernel estimation in single ellipse fitting.
    • Developed a coupled ellipse fitting method using data association and second-order cone programming.

    Main Results:

    • The proposed MCC-VC method demonstrates superior performance against existing techniques.
    • Achieved significantly better results on both simulated and real-world image data.
    • Successfully handled scenarios with absent data-to-ellipse associations.

    Conclusions:

    • The MCC-VC based ellipse fitting method offers enhanced robustness and accuracy.
    • The approach is effective for both single and coupled ellipse detection in the presence of noise.
    • This work provides a valuable advancement for computer vision and image analysis tasks.