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    This study introduces automatic filter threshold selection for morphological attribute profiles using Granulometric Characteristic Functions (GCFs). This method reduces redundant image data and computational time in multi-scale analysis.

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    Area of Science:

    • Image analysis
    • Computer vision
    • Remote sensing

    Background:

    • Morphological attribute profiles (MAPs) are crucial for multi-scale image analysis.
    • Selecting optimal filter parameters for MAPs remains a challenge, often requiring manual tuning.
    • Existing methods can lead to redundant information and increased computational costs.

    Purpose of the Study:

    • To present a framework for automatic filter threshold selection in MAPs.
    • To leverage Granulometric Characteristic Functions (GCFs) for efficient threshold identification.
    • To reduce redundancy and computational time in image decomposition.

    Main Methods:

    • Utilizing Granulometric Characteristic Functions (GCFs) to analyze image simplification by morphological filters.
    • Exploiting the Tree of Shapes representation for efficient GCF computation.
    • Developing a framework for automatic selection of representative and non-redundant thresholds.

    Main Results:

    • Successfully automated the selection of filter thresholds for MAPs.
    • Demonstrated reduction in redundant information within the generated profiles.
    • Achieved decreased computational time compared to manual selection methods.

    Conclusions:

    • The proposed GCF-based framework effectively automates threshold selection for MAPs.
    • This approach enhances the efficiency and reduces redundancy in multi-scale image analysis.
    • The method shows promising results on remote sensing data classification.