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    • 39-Issue 7
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    • 39-Issue 7
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    Multi-scale Information Assembly for Image Matting

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    v39i7pp565-574.pdf (7.512Mb)
    Date
    2020
    Author
    Qiao, Yu
    Liu, Yuhao
    Zhu, Qiang
    Yang, Xin
    Wang, Yuxin
    Zhang, Qiang
    Wei, Xiaopeng
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    Abstract
    Image matting is a long-standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images.We argue that the foreground objects can be represented by different-level information, including the central bodies, large-grained boundaries, refined details, etc. Based on this observation, in this paper, we propose a multi-scale information assembly framework (MSIA-matte) to pull out high-quality alpha mattes from single RGB images. Technically speaking, given an input image, we extract advanced semantics as our subject content and retain initial CNN features to encode different-level foreground expression, then combine them by our well-designed information assembly strategy. Extensive experiments can prove the effectiveness of the proposed MSIA-matte, and we can achieve state-of-the-art performance compared to most existing matting networks.
    BibTeX
    @article {10.1111:cgf.14168,
    journal = {Computer Graphics Forum},
    title = {{Multi-scale Information Assembly for Image Matting}},
    author = {Qiao, Yu and Liu, Yuhao and Zhu, Qiang and Yang, Xin and Wang, Yuxin and Zhang, Qiang and Wei, Xiaopeng},
    year = {2020},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14168}
    }
    URI
    https://doi.org/10.1111/cgf.14168
    https://diglib.eg.org:443/handle/10.1111/cgf14168
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    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
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