%0 Journal Article
%T Improved dynamic target detection algorithm based on KGMM<br>基于KGMM改进的动态目标检测算法
%A GUO Chun-feng
%A HE Jian-nong
%A <br>郭春凤
%A 何建农
%J 计算机应用研究
%D 2012
%I 
%X The online K-means clustering method for initialization Gaussian mixture model KGMM with respect to run time, space complexity and noise have some disadvantages, this paper proposed an improved method of detection based on KGMM, added the variance factor to the C-means clustering criterion to initialize Gaussian mixture model. It effectively solved the problem that a pixel value may belong to different distribution classes driving different probabilities, and improved the flexibility of detection; improved the matching criterion of Gaussian model and increased the accuracy of the detection algorithm; established mixed Gaussian distribution for every other pixel point, it reduced the amount of Gaussian model, saved storage space, and reduced the running time of the algorithm. The experimental results show that the effect of the improved detection algorithm is more ideal.
%K Gaussian mixture model
%K C-means cluster
%K dynamic object detecting<br>混合高斯模型
%K C-均值聚类
%K 动态目标检测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=E82D2154DD923E6E623A5F5E560A5B48&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=5D311CA918CA9A03&sid=E6F9016E859B9026&eid=0734A8CD23EB75CA&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=9