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求查一下这一篇文章是否SCI检索，谢谢。 已有1人参与
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| Hui Wang, Ting-Zhu Huang, Zhi Xu, Yugang Wang. A two-stage image segmentation via global and local region active contours. Neurocomputing, 2016, 205:130-140. |
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zhangrong441(paperhunter代发): 金币+5, 鼓励交流 2016-08-17 09:37:29
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zhangrong441(paperhunter代发): 金币+5, 鼓励交流 2016-08-17 09:37:29
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恭喜你 检索情况如下： A two-stage image segmentation via global and local region active contours 作者:Wang, H (Wang, Hui)[ 1,2 ] ; Huang, TZ (Huang, Ting-Zhu)[ 1 ] ; Xu, Z (Xu, Zhi)[ 1 ] ; Wang, YG (Wang, Yugang)[ 1 ] NEUROCOMPUTING 卷: 205 页: 130-140 DOI: 10.1016/j.neucom.2016.03.050 出版年: SEP 12 2016 查看期刊信息 摘要 Based on popular active contours, this paper proposes a novel two-stage image segmentation method, which incorporates the global and local image region fitting energies. In the first stage, according to the global region active contour, we preliminarily segment the image by globally using the Gaussian distribution, which can rapidly get a coarse segmentation result. Subsequently, by employing a window function, we further segment the image by using the local region active contour, where we use the final active contour of the first stage as the initialization. Compared with the first stage, the local object details are accurately segmented in the second stage, which can be considered as an accurate segmentation result. Due to the suitable initialization from the first stage, the second stage works well in accurately segmenting the image, especially in local details. To regularize the level set function, we introduce a Laplace operator, which efficiently eliminates the expensive re-initialization process of traditional level set methods. Compared with the state-of-the-art methods, experiment results demonstrate the effectiveness and performance of the proposed method with applications to synthetical and real-world images, which usually contain noise, blurry boundaries, and intensity inhomogeneities. (C) 2016 Elsevier B.V. All rights reserved. 关键词 作者关键词:Image segmentation; Active contours; Level set method; Two-stage KeyWords Plus:LEVEL SET EVOLUTION; FITTING ENERGY; CURVE EVOLUTION; MODEL; DRIVEN; FLOW; ALGORITHMS; EFFICIENT; MUMFORD; SNAKES 作者信息 通讯作者地址: Huang, TZ (通讯作者) Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China. 地址: [ 1 ] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China [ 2 ] Anshun Univ, Sch Math & Phys, Anshun 561000, Guizhou, Peoples R China 电子邮件地址:wanghui561403@163.com; tingzhuhuang@126.com 基金资助致谢 基金资助机构 授权号 973 Program 2013CB329404 NSFC 61370147 61170311 Sichuan Province Sci. & Tech. Research Project 2012GZX0080 查看基金资助信息 出版商 ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 类别 / 分类 研究方向:Computer Science Web of Science 类别:Computer Science, Artificial Intelligence 文献信息 文献类型:Article 语种:English 入藏号: WOS:000378952500013 ISSN: 0925-2312 eISSN: 1872-8286 期刊信息 Impact Factor (影响因子): Journal Citation Reports® 其他信息 IDS 号: DQ1HQ Web of Science 核心合集中的 "引用的参考文献": 45 Web of Science 核心合集中的 "被引频次": 0 |
2楼2016-08-17 09:35:55
