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    • 39-Issue 7
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    Generating High-quality Superpixels in Textured Images

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    v39i7pp421-431.pdf (10.35Mb)
    Date
    2020
    Author
    Zhang, Zhe
    Xu, Panpan
    Chang, Jian
    Wang, Wencheng
    Zhao, Chong
    Zhang, Jian Jun ORCID
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    Abstract
    Superpixel segmentation is important for promoting various image processing tasks. However, existing methods still have difficulties in generating high-quality superpixels in textured images, because they cannot separate textures from structures well. Though texture filtering can be adopted for smoothing textures before superpixel segmentation, the filtering would also smooth the object boundaries, and thus weaken the quality of generated superpixels. In this paper, we propose to use the adaptive scale box smoothing instead of the texture filtering to obtain more high-quality texture and boundary information. Based on this, we design a novel distance metric to measure the distance between different pixels, which considers boundary, color and Euclidean distance simultaneously. As a result, our method can achieve high-quality superpixel segmentation in textured images without texture filtering. The experimental results demonstrate the superiority of our method over existing methods, even the learning-based methods. Benefited from using boundaries to guide superpixel segmentation, our method can also suppress noise to generate high-quality superpixels in non-textured images.
    BibTeX
    @article {10.1111:cgf.14156,
    journal = {Computer Graphics Forum},
    title = {{Generating High-quality Superpixels in Textured Images}},
    author = {Zhang, Zhe and Xu, Panpan and Chang, Jian and Wang, Wencheng and Zhao, Chong and Zhang, Jian Jun},
    year = {2020},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14156}
    }
    URI
    https://doi.org/10.1111/cgf.14156
    https://diglib.eg.org:443/handle/10.1111/cgf14156
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA