%0 Journal Article %T Markov Random Field in Visual Information Processing
视觉信息处理中的马尔可夫随机场 %A TAO Lin-mi %A WANG Qi-fan %A DI Hui-jun %A
陶霖密 %A 王奇凡 %A 邸慧军 %J 中国图象图形学报 %D 2009 %I %X Probabilistic graphical models (PGM) is widely applied in visual information processing for the intrinsic uncertainty in visual information, and followed by a group of researchers recently. PGM offers a number of advantages for resolving variety problems in visual information processing, in which Markov Random Field (MRF) can be used to model pixel level information processing based on the development of high efficiency inference algorithms. In this paper, we shortly introduced concepts of PGM, and gave detailed analysis and discussion on the definition, features and inference of MRF followed by typical examples of its application in computer vision. %K markov random field(MRF) %K probabilistic graphical model(PGM) %K bayesian network %K belief propagation(BP) %K graph cut(GC) %K energy minimization
马尔可夫随机场 %K 概率图模型 %K 贝叶斯网络 %K 信息传播 %K 图像切割 %K 能量最小化 %U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=0D75DDCA26349A7BA4EB75EF3006C26B&yid=DE12191FBD62783C&vid=F3583C8E78166B9E&iid=9CF7A0430CBB2DFD&sid=3183893ED5218CD5&eid=A22DD5EE0F220B37&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=20