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  • Robust lip region segmentation for lip images with complex background

    Author(s)
    Wang, Shi-Lin
    Lau, Wing-Hong
    Liew, Alan Wee-Chung
    Leung, Shu-Hung
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2007
    Metadata
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    Abstract
    Robust and accurate lip region segmentation is of vital importance for lip image analysis. However, most of the current techniques break down in the presence of moustaches and beards. With moustaches and beards, the background region becomes complex and inhomogeneous. We propose in this paper a novel multi-class, shape-guided FCM (MS-FCM) clustering algorithm to solve this problem. For this new approach, one cluster is set for the object, i.e. the lip region, and a combination of multiple clusters for the background which generally includes the skin region, lip shadow or beards. The proper number of background clusters is ...
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    Robust and accurate lip region segmentation is of vital importance for lip image analysis. However, most of the current techniques break down in the presence of moustaches and beards. With moustaches and beards, the background region becomes complex and inhomogeneous. We propose in this paper a novel multi-class, shape-guided FCM (MS-FCM) clustering algorithm to solve this problem. For this new approach, one cluster is set for the object, i.e. the lip region, and a combination of multiple clusters for the background which generally includes the skin region, lip shadow or beards. The proper number of background clusters is derived automatically which maximizes a cluster validity index. A spatial penalty term considering the spatial location information is introduced and incorporated into the objective function such that pixels having similar color but located in different regions can be differentiated. This facilitates the separation of lip and background pixels that otherwise are inseparable due to the similarity in color. Experimental results show that the proposed algorithm provides accurate lip-background partition even for the images with complex background features like moustaches and beards
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    Journal Title
    Pattern Recognition
    Volume
    40
    Issue
    12
    Publisher URI
    http://www.elsevier.com/wps/find/journaldescription.cws_home/328/description#description
    DOI
    https://doi.org/10.1016/j.patcog.2007.03.016
    Subject
    Information systems
    Publication URI
    http://hdl.handle.net/10072/17058
    Collection
    • Journal articles

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