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GhostingNet: A Novel Approach for Glass Surface Detection With Ghosting Cues.

Tao Yan, Jiahui Gao, Ke Xu

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    This study introduces GhostingNet, a novel method for detecting glass surfaces by leveraging ghosting effects. The approach utilizes a new dataset and a unique detection module, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Material Science

    Background:

    • Glass surfaces exhibit ghosting effects due to double reflections from their two contact surfaces.
    • Existing methods for glass surface detection may not fully exploit these intrinsic ghosting properties.

    Purpose of the Study:

    • To propose a novel method for accurate glass surface detection by utilizing ghosting effects.
    • To introduce a new dataset specifically designed for glass surface ghosting detection.

    Main Methods:

    • Formulated a ghosting image formation model to capture reflection intensity and spatial relationships.
    • Constructed the Glass Surface Ghosting Dataset (GSGD) with images, ghosting masks, and glass surface masks.
    • Developed GhostingNet, comprising a Ghosting Effects Detection (GED) module with a Double Reflection Estimation (DRE) block and a Glass Surface Detection (GSD) module.

    Main Results:

    • The proposed ghosting image formation model effectively describes reflection characteristics.
    • GhostingNet successfully detects ghosting effects and uses them to guide glass surface detection.
    • Experimental results show GhostingNet outperforms state-of-the-art methods in glass surface detection.

    Conclusions:

    • Leveraging ghosting effects offers a promising approach for robust glass surface detection.
    • The developed GhostingNet method and GSGD dataset provide valuable tools for advancing research in this area.
    • The proposed method demonstrates superior performance, paving the way for improved applications in computer vision and material analysis.