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题名: Globally optimal and region scalable CV model on automatic target segmentation
作者: Fan HJ(范慧杰) ; Dong W(董威) ; Tang YD(唐延东)
作者部门: 机器人学研究室
会议名称: 3rd International Conference on Intelligent Robotics and Applications, ICIRA 2010
会议日期: November 10-12, 2010
会议地点: Shanghai, China
会议录: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
会议录出版者: Springer Verlag
会议录出版地: Heidelberg, Germany
出版日期: 2010
页码: 600-608
收录类别: CPCI(ISTP) ; EI
ISSN号: 0302-9743
ISBN号: 9783642165832
关键词: Data handling ; Drop breakup ; Level measurement ; Numerical methods ; Optimization ; Robotics
摘要: This paper proposes a new Globally Optimal and Region Scalable Chan-Vese model (GRCV model), which combines the advantages of both local and global intensity information. Intensity information in local regions is emphasized by adding a kernel function in the data fitting term, which thereby enables the proposed model to extract the fine structure in local region. The global information is also considered by adding one data fitting term in Piecewise Constant (PC) model to guarantee that proposed model can successfully segment complete target regions from intensity inhomogeneous images. We give the selection rules of combining the different terms under different target and background intensities: when the target is intensity inhomogeneous while the background is homogeneous, we choose the data fitting term which effects on the outside region of the evolution contour; conversely, we choose the other term. Experiments have been done on different images to compare the effectiveness of our methods with that of the classical PC model, Li's Local Binary Fitting (LBF) model, and Wang's Local and Global Intensity Fitting (LGIF) model. Comparison results show that our model obtains more satisfactory segmentation results. Moreover, it is robust to the curve initialization and noise in images. © 2010 Springer-Verlag.
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内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8724
Appears in Collections:机器人学研究室_会议论文

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