NDLI: A robust improved Chan-Vese model based on Gaussian regularizing level set

Content Provider	IEEE Xplore Digital Library
Author	Nengyuan Pan Zhengshou Feng MeiQing Wang
Copyright Year	2011
Description	Author affiliation: College of Mathematics and Computer Science, Fuzhou University, Fujian 350108, China (Nengyuan Pan; Zhengshou Feng; MeiQing Wang)
Abstract	In this paper, a new robust improved Chan-Vese (ICV) model is proposed for image segmentation, which is built based on the techniques of curve evolution, signed pressure force (SPF) function and level set method. Compared with the ICV model, the proposed method is more robust to the location of the initial contour. Similar to the ICV model, a Gaussian regularizing level set method (GRLSM) is used to reduce the computational cost. Experimental results on some synthetic and real images show that our model is efficiency. Moreover, comparisons with the ICV model show that our model is more robust to the location of the initial contour.
Starting Page	1150
Ending Page	1154
File Size	371099
Page Count	5
File Format	PDF
ISBN	9781424493043
e-ISBN	9781424493067
DOI	10.1109/CISP.2011.6100430
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Level set Computational modeling Capacitance-voltage characteristics GRLSM SPF Chan-Vese model Robustness Mathematical model ICV Active contours
Content Type	Text
Resource Type	Article

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A robust improved Chan-Vese model based on Gaussian regularizing level set