%0 Journal Article
%T Tracking object contour based on multiple features<br>基于多特征的目标轮廓跟踪*
%A WU Xi-yan
%A DONG Fang-min
%A LIU Yong
%A CAI Linga
%A <br>吴西燕
%A 董方敏
%A 刘勇
%A 蔡岭a
%J 计算机应用研究
%D 2011
%I 
%X Abstract: In this paper, Kernel density estimate is used to construct the probability distributions models for color feature and texture feature. With these two models, Bayesian model calculates the posterior probabilities of the object and background pixels. Furthermore, a new region energy functional is proposed to count the energy of the object and the background pixels respectively.So that minimum energy contour coincides with the object contour. At last, the gradient descent flow derived from the variational reduces the contour energy and converge to the object boundary by evolving it. Different experimental results show that the proposed algorithm can track the rigid object contour and non-rigid object contour in image sequences efficiently.
%K track object contour
%K Kernel density estimate
%K Bayesian model
%K energy functional
%K level set<br>目标轮廓跟踪
%K 核密度方法
%K 贝叶斯模型
%K 能量泛函
%K 水平集
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=A5986DF7DF3DD0D02C669CD8C8D6E1D8&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=94C357A881DFC066&sid=D68286E0C08ACACF&eid=7373B0D450113B06&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=13