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Automatic Reference Color Selection for Adaptive Mathematical Morphology and Application in Image Segmentation.

Huang-Chia Shih, En-Rui Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 9, 2016
    PubMed
    Summary

    A new Automatic Reference Color Selection (ARCS) scheme optimizes adaptive mathematical morphology (MM) for color image segmentation. This method improves distance measurement scope and threshold determination sensitivity for better segmentation results.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Mathematical morphology (MM) is popular for image processing due to its simplicity.
    • Traditional MM methods often neglect optimal reference color determination, leading to suboptimal distance measurements in segmentation.

    Purpose of the Study:

    • To introduce a novel Automatic Reference Color Selection (ARCS) scheme for adaptive mathematical morphology.
    • To enhance color image segmentation by optimizing reference color selection for MM.

    Main Methods:

    • The proposed ARCS scheme determines ideal reference colors for MM in color image segmentation.
    • Utilized 1D histogram-based modeling from 3D color spaces (RGB, HSI) and 2D color models (HS, CbCr, IBy).
    • Employed quartile analysis for robust threshold determination and evaluated using quantitative indices and cross-validation.

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    Main Results:

    • The ARCS scheme demonstrated improved distance measurement scope compared to methods using only black as reference.
    • Threshold determination showed reduced sensitivity to image context variations.
    • Experiments confirmed the effectiveness of ARCS in color-based image segmentation using MM.

    Conclusions:

    • The ARCS scheme provides an effective solution for reference color determination in adaptive mathematical morphology.
    • This method significantly enhances the performance of color image segmentation applications.