NDLI: A convex approaches toward global minimization for fast multiphase image segmentation

Content Provider	IEEE Xplore Digital Library
Author	Jiangxiong Fang Zhiping Wen Huaxiang Liu Liting Zhang Jun Liu Liming Rao
Copyright Year	2015
Description	Author affiliation: Jiangxi Province Key Lab. for Digital Land, East China Inst. of Technol., Nanchang, China (Jiangxiong Fang; Zhiping Wen; Huaxiang Liu; Liting Zhang) \|\| Sch. of Nucl. Eng. & Geophys., East China Inst. of Technol., Nanchang, China (Jun Liu; Liming Rao)
Abstract	The study is to investigate a fast multiphase image segmentation model from a statistical framework. The globally convex image segmentation method and the split Bregman method are incorporated into the model by maximizing the posterior image densities over all possible partitions of the image plane. The proposed model is robust with respect to noise based on Gaussian kernel function and can avoid a n unambiguous segmentation due to a new representation of a partition of an image domain into a number of regions. Given these advantages, the proposed method can get good performance and experiments show promising segmentation results on both synthetic and real images.
Starting Page	547
Ending Page	551
File Size	374414
Page Count	5
File Format	PDF
e-ISBN	9781467371896
DOI	10.1109/CAC.2015.7382561
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Global minimization Image segmentation Variational model Computational modeling Level set Biological system modeling Minimization Convex formulation Bayes methods Active contours
Content Type	Text
Resource Type	Article

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