Explicit Order Model for Region-based Level Set Segmentation

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Explicit Order Model for Region-based Level Set Segmentation
	Wang, Lingfeng; Pan, Chunhong
	2015
会议名称	IEEE International Conference on Acoustics, Speech & Signal Processing
会议录名称	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
会议日期	2015
会议地点	South Brisbane
摘要	Region-based level set methods have been widely used for image segmentation. Among them, the method based on local binary fitting (LBF) model is an efficient one. Unfortunately, LBF model is sensitive to initial contour. To overcome this disadvantage, we propose two explicit order models, i.e., the global order preserving and local order smoothness models. The global order preserving model ensures that the binary fitting values have the same order globally, while the local order smoothness model requires that these orders are smooth locally. With these two models, our segmentation results are not sensitive to initializations. Experimental results on synthetic and real images show desirable performances of our method, as compared with the state-of-the-art approaches.
关键词	Image Segmentation Level Set Region-based Cv Model Lbf Model
收录类别	EI
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/10763
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wang, Lingfeng,Pan, Chunhong. Explicit Order Model for Region-based Level Set Segmentation[C],2015.

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