NDLI: A Bayesian Approach to Clustering Matting Components in Spectral Matting

Content Provider	IEEE Xplore Digital Library
Author	Ge Wang Liang-Hao Wang Dong-Xiao Li Ming Zhang
Copyright Year	2013
Description	Author affiliation: Inst. of Inf. & Commun. Eng., Zhejiang Univ., Hangzhou, China (Ge Wang; Liang-Hao Wang; Dong-Xiao Li; Ming Zhang)
Abstract	This paper proposes to apply Bayesian principle to clustering matting components in spectral matting. Spectral matting is a useful and effective technique for digital image matting. A crucial issue of spectral matting is how to cluster the computed matting components, which compose the final alpha matte. In this paper, a new clustering strategy based on Bayesian decision theory is proposed to solve this problem. In our algorithm, given the input scribbles as a trimap, the foreground and background information is propagated outward into unknown region iteratively, which makes up the calculated foreground/ background distribution function. Then the Bayesian decision theory is adopted to cluster the matting components. The matting components which are clustered into foreground are summed up to generate the final alpha matte.
Starting Page	287
Ending Page	290
File Size	482294
Page Count	4
File Format	PDF
ISBN	9780769550503
DOI	10.1109/ICIG.2013.76
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Digital images Production Bayesian matting Educational institutions Probability distribution Bayes methods Mathematical model matting components
Content Type	Text
Resource Type	Article

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