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    Area of Science:

    • Computer Vision
    • Image Processing
    • Pattern Recognition

    Background:

    • Accurate detection of geometric shapes like line segments and elliptical arcs is crucial for image analysis.
    • Existing methods often require parameter tuning and can produce numerous false detections.

    Purpose of the Study:

    • To develop a robust line segment and elliptical arc detector with reduced false detections.
    • To implement a parameter-free approach for model validation and selection.

    Main Methods:

    • A novel statistical criterion based on the a contrario theory is proposed.
    • The criterion is used for both validating the presence of shapes and selecting the best model (line segment or elliptical arc).
    • The detector operates on grey-scale image regions without requiring any parameter adjustments.

    Main Results:

    • The proposed detector significantly reduces false detections across various image types.
    • Experimental results demonstrate superior performance compared to state-of-the-art detectors.
    • The approach is effective on both synthetic and real-world images.

    Conclusions:

    • The a contrario-based detector offers a reliable and parameter-free solution for line segment and elliptical arc detection.
    • This method enhances the accuracy and efficiency of image analysis tasks.
    • The approach shows strong potential for applications in computer vision and image processing.