标题:A Novel Selective Segmentation Model for Images with Intensity Inhomogeneity
作者:Li Jiaojiao; Li Shurong; Wang Xueqin; Zang Renlin
通讯作者:Li, JJ
作者机构:[Li Jiaojiao; Li Shurong; Zang Renlin] China Univ Petr East China, Coll Informat & Control Engn, Qingdao 266580, Peoples R China.; [Wang Xueqin] Sha 更多
会议名称:35th Chinese Control Conference (CCC)
会议日期:JUL 27-29, 2016
来源:PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016
出版年:2016
页码:4166-4171
关键词:Selective Segmentation; Images with Intensity Inhomogeneity; Geometrical; Constraint; LBF Model; AOS Method
摘要:Selective image segmentation is a very important and practical procedure in image processing. However, the existing selective segmentation model can not segment images with intensity inhomogeneity and fuzzy edge images accurately. In this paper a novel model which has better performance for images with intensity inhomogeneity and fuzzy edge images is proposed. The establishment of the model is based on the techniques of curve evolution, local statistic information and level set method. The stopping term in the new model with geometrical constraint is based on the LBF (Local Binary Fitting) active contour model. This paper applies the AOS (Additive Operator Splitting) method to speed up the segmentation. Experimental results on synthetic and real images clearly show that the novel model can segment the target more accurately, especially with respect to images with intensity inhomogeneity, weak edges and noise.
收录类别:CPCI-S
资源类型:会议论文
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