ORCID Profile
0000-0003-2059-120X
Current Organisation
City University of Hong Kong
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Publisher: Elsevier BV
Date: 12-2002
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-753-9.CH014
Abstract: Recently, lip image analysis has received much attention because the visual information extracted has been shown to provide significant improvement for speech recognition and speaker authentication, especially in noisy environments. Lip image segmentation plays an important role in lip image analysis. This chapter will describe different lip image segmentation techniques, with emphasis on segmenting color lip images. In addition to providing a review of different approaches, we will describe in detail the state-of-the-art classification-based techniques recently proposed by our group for color lip segmentation: “Spatial fuzzy c-mean clustering” (SFCM) and “fuzzy c-means with shape function” (FCMS). These methods integrate the color information along with different kinds of spatial information into a fuzzy clustering structure and demonstrate superiority in segmenting color lip images with natural low contrast in comparison with many traditional image segmentation techniques.
Publisher: Institution of Engineering and Technology (IET)
Date: 2000
DOI: 10.1049/EL:20000931
Publisher: Institution of Engineering and Technology (IET)
Date: 2000
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-60566-186-5.CH005
Abstract: As the first step of many visual speech recognition and visual speaker authentication systems, robust and accurate lip region segmentation is of vital importance for lip image analysis. However, most of the current techniques break down when dealing with lip images with complex and inhomogeneous background region such as mustaches and beards. In order to solve this problem, a Multi-class, Shapeguided FCM (MS-FCM) clustering algorithm is proposed in this chapter. In the proposed approach, one cluster is set for the lip region and a combination of multiple clusters for the background which generally includes the skin region, lip shadow or beards. With the spatial distribution of the lip cluster, 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. Experimental results show that the proposed algorithm provides accurate lip-background partition even for the images with complex background features.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2008
Publisher: SPIE
Date: 21-05-1993
DOI: 10.1117/12.144759
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2003
Publisher: Elsevier BV
Date: 12-2007
Publisher: IEEE
Date: 2006
No related grants have been discovered for Shu-Hung LEUNG.