CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Explicit Order Model for Region-based Level Set Segmentation
Wang, Lingfeng; Pan, Chunhong
2015
Conference NameIEEE International Conference on Acoustics, Speech & Signal Processing
Source PublicationIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference Date2015
Conference PlaceSouth Brisbane
AbstractRegion-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.
KeywordImage Segmentation Level Set Region-based Cv Model Lbf Model
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10763
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Wang, Lingfeng,Pan, Chunhong. Explicit Order Model for Region-based Level Set Segmentation[C],2015.
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