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    This study compares algorithms for computing image filtering connected operators using max-tree and min-tree representations. It provides a decision tree and open-source code to help users select the most efficient algorithm for their needs.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Geometry

    Background:

    • Connected operators are essential morphological tools for image filtering, preserving existing contours.
    • Max-tree and min-tree representations are fundamental to connected operators.
    • Existing algorithms for computing these trees lack comprehensive comparative analysis.

    Purpose of the Study:

    • To conduct an in-depth comparison of existing algorithms for computing max-trees and min-trees.
    • To evaluate both sequential and parallel algorithm performance.
    • To provide guidance for selecting the most efficient algorithm based on user requirements.

    Main Methods:

    • Benchmarking of sequential and parallel algorithms for connected operator computation.
    • Analysis of execution times based on image size, pixel quantization, and thread count.
    • Development of variations to enhance algorithm efficiency.

    Main Results:

    • An exhaustive comparison of connected operator algorithms is presented.
    • Performance metrics for various algorithms under different conditions are detailed.
    • A decision tree is proposed to aid algorithm selection.

    Conclusions:

    • The study offers a framework for understanding and selecting optimal algorithms for connected operators.
    • Accessible source code and an online demo promote reproducible research.
    • Efficient algorithm selection is crucial for practical image processing applications.