高斯·马尔科夫序列实验表明，该算法较好地实现了全局最优，并有助于克服对初始码书较为敏感的缺点。
The experiments of Gauss Markov sequences show that the algorithm has better achieved the global optimal point and helps overcome the shortcoming of the sensitivity to initial codebook.
三重马尔科夫随机场(TMF)模型非常适合处理非平稳、非高斯图像的分割问题。
Triplet Markov random fields (TMF) model is suitable for dealing with multi-class segmentation of non-stationary, non-Gaussian SAR images.
把运动矢量场建模为高斯马尔科夫随机场，对丢失图像块的运动矢量采用最大后验概率方法恢复，其权值能够根据空间和时间信息而自适应选择。
The motion vectors of the damaged image macroblocks can be recovered adaptively by Maximum A Posteriori(MAP), and the weight is selected adaptively based o.
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