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Low-level feature extraction for edge detection using genetic programming.

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    This summary is machine-generated.

    This study introduces a genetic programming (GP) system for automatic edge detector construction. The GP approach optimizes pixel selection, improving edge detection accuracy and noise rejection in natural images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Edge detection is crucial in image analysis but traditionally involves a subjective moving window approach.
    • Fixed window sizes present a trade-off between edge localization accuracy and noise rejection.
    • Developing automatic techniques for edge detector construction is desirable for diverse applications.

    Purpose of the Study:

    • To propose a genetic programming (GP) system for automatically constructing novel, low-level subjective edge detectors.
    • To investigate the selection of pixels by the GP system for edge detection in natural images.
    • To analyze the performance of GP-derived edge detectors compared to traditional methods.

    Main Methods:

    • A genetic programming (GP) system was developed to automatically search for optimal pixel configurations (a center pixel and its neighbors).
    • These selected pixels were used to construct new edge detectors, avoiding fixed window limitations.
    • Linear and second-order filters were derived from frequently occurring pixels identified by the GP system.

    Main Results:

    • The proposed GP system demonstrated good performance in edge detection tasks.
    • Automatically selected pixels by GP mitigate edge blurring from large windows and noise from small windows.
    • Filters constructed from GP-selected pixels were found to be compact yet effective.

    Conclusions:

    • The GP system offers an effective method for automatic edge detector construction.
    • GP-based pixel selection provides a flexible and robust approach to edge detection.
    • The selected pixel sets are efficient and sufficient for creating high-performing edge detectors.