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基于EM和GMM相结合的自适应灰度图像分割算法
引用本文:罗胜,郑蓓蓉,叶忻泉.基于EM和GMM相结合的自适应灰度图像分割算法[J].光子学报,2009,38(6):1581-1585.
作者姓名:罗胜  郑蓓蓉  叶忻泉
作者单位:温州大学,机电工程学院,浙江,温州,325035
基金项目:国家重点学科项目,上海市重点学科建设项目 
摘    要:提出一种阈值自适应、EM方法估计GMM参量的图像分割算法,能够根据图像的内容结合区域和边界两方面的信息自适应地选择阈值,精确地进行图像边界分割.算法首先提取图像的边界,然后根据边界的直方图计算图像的可分割性,由可分割性确定EM方法的阈值进行GMM分割,最后合并图像的近似区域.实验数据表明,相比其它图像分割算法,以及固定阈值的传统EM算法,本算法的分割结果更为准确.

关 键 词:图像分割  混合高斯模型  期望最大  自适应阈值
收稿时间:2008-09-06
修稿时间:2008-10-23

Image Segment Based on the Self-adaptive Threshold EM and GMM Algorithm
LUO Sheng,ZHENG Bei-rong,YE Xin-quan.Image Segment Based on the Self-adaptive Threshold EM and GMM Algorithm[J].Acta Photonica Sinica,2009,38(6):1581-1585.
Authors:LUO Sheng  ZHENG Bei-rong  YE Xin-quan
Institution:(School of Mechanical &|Electrical Engineering,Wenzhou University,Zhejiang,Wenzhou 325035,China)
Abstract:A new segment algorithm which was self-adaptive to the image content,combining patch-based information with edge cues under a probabilistic framework was presented.Edges were detected firstly.Then a histogram and the segmentable measure of the imgae were computed.Later EM algorithm was adopted to estimate the mixture of multiple Gaussians which was built as a statistical model on spatial features.Lastly the adjacent regions with similar properties are united to one.The novelty of this algorithm is that the ...
Keywords:Image Segment  Gaussian mixture model (GMM)    Expectation Maximization (EM) algorithm  Self adaptive threshold
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