It is a markov network , we confirm the transfer rule of this network based on two kind of relationship , one is the image and the scene , the other is the current scene and its neighbour . learning parameters of this network come from training examples , we can obtain a exact local maximum of the posterior probability for the scene , thereby we generate a effective edge detection result for original blurry importing image 它的体系结构是一个马尔可夫网络,根据图像与景物、景物与景物之间的联系来确定网络上的信息传递规则,并从大量的事例中学习这些网络参数,可以高效地为所求景物找到一个精确的后验概率的局部最大值,从而为原模糊图像获得高效的边缘标记结果。
For instance , a new property of network traffic ( self - similarity ) has been observed in diverse networking contexts . some old protocols , policies and evaluate methods which are based on previous markov network model are not suitable for this self - similar traffic . and when tcp data share bandwidth with udp data that is popular for multimedia applications over the internet , it also causes fairness problem 多媒体业务流会给整个网络的性能带来极大的影响,如:多媒体业务导致网络流自相似现象加重,使网络的原有的markov模型基础上的协议、策略,及其评价方法不够准确,导致丢失率上升,网络性能下降;多媒体业务中使用的大量udp包与普通数据流tcp包共存时对公平性的影响等等。