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题名: Self-Adaptive Multiple Evolution algorithms for Image Segmentation using Multilevel Thresholding
作者: Sun LL(孙丽玲); Hu JT(胡静涛); Zhang QC(张巧翠); Chen HN(陈瀚宁)
作者部门: 信息服务与智能控制技术研究室
会议名称: 10th International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2015)
会议日期: September 25-28, 2015
会议地点: Hefei, China
会议录: Bio-Inspired Computing -- Theories and Applications
会议录出版者: Springer Verlag
会议录出版地: Berlin
出版日期: 2015
页码: 400-410
收录类别: EI ; CPCI(ISTP)
ISSN号: 1865-0929
ISBN号: 978-3-662-49013-6
关键词: Multiple evolution algorithms ; Multilevel threshold ; Image segmentation
摘要: Multilevel thresholding based on Otsu method is one of the most popular image segmentation techniques. However, when the number of thresholds increases, the consumption of CPU time grows exponentially. Although the evolution algorithms are helpful to solve this problem, for the high-dimensional problems, the Otsu methods based on the classical evolution algorithms may get trapped into local optimal or be instability due to the inefficiency of local search. To overcome such drawback, this paper employs the self-adaptive multiple evolution algorithms (MEAs), which automatically protrudes the core position of the excellent algorithm among the selected algorithms. The tests against 10 benchmark functions demonstrate that this multi-algorithms is fit for most problems. Then, this optimizer is applied to image multilevel segmentation problems. Experimental results on a variety of images provided by the Berkeley Segmentation Database show that the proposed algorithm can accurately and stably solve this kind of problems.
语种: 英语
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/17381
Appears in Collections:信息服务与智能控制技术研究室_会议论文

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Recommended Citation:
Sun LL,Hu JT,Zhang QC,et al. Self-Adaptive Multiple Evolution algorithms for Image Segmentation using Multilevel Thresholding[C]. 见:10th International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2015). Hefei, China. September 25-28, 2015.
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文件名: Self-Adaptive Multiple Evolution algorithms for Image Segmentation using Multilevel Thresholding.pdf
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