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    This study introduces a novel ellipse detection method using a three-intersection-chord-invariant. The approach enhances accuracy and stability in computer vision by refining arc segment combinations and employing geometric constraints for precise ellipse identification.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Accurate ellipse detection in real-world images remains a significant challenge.
    • Existing methods struggle with stability and directness, particularly with complex image data.

    Purpose of the Study:

    • To propose a robust and accurate ellipse detection algorithm.
    • To improve the reliability of ellipse detection in computer vision applications.

    Main Methods:

    • Utilizing a three-intersection-chord-invariant model for ellipse detection.
    • Employing PCA minimum bounding box for refined line segment screening.
    • Implementing multi-scale inflexion point detection and arc segment refinement.
    • Applying midpoint distance and quadrant constraints to filter combinations.
    • Performing ellipse validation and clustering for high-precision results.

    Main Results:

    • The proposed method achieves refined line segment screening and avoids over-segmentation.
    • Geometric constraints effectively reduce incorrect arc segment combinations.
    • A robust initial ellipse set is obtained through the invariant model.
    • Experimental validation on multiple datasets demonstrates high-precision ellipse detection.

    Conclusions:

    • The developed ellipse detection method offers improved accuracy and stability.
    • The three-intersection-chord-invariant model provides strong geometric constraints for reliable detection.
    • The algorithm shows significant potential for various computer vision tasks.