针对马尔可夫随机场在红外图像分割方面存在的问题，给出了一种基于混合高斯模型的三马尔可夫场红外图像分割算法。
Due to the problems to infrared image segmentation using Markov random fields, a method for infrared image segmentation based on triplet Markov fields using mixture gauss model was proposed.
模拟结果证实了高斯平稳场-马尔可夫场的正确性和谱模型的有效性。
The results show the correctness of Gaussian stationary field-Markov field and the validity of spectral model.
本文将混合高斯分布应用于一种非齐次隐含马尔可夫模型——简化的基于段长分布的隐含马尔可夫模型。
Gaussian Mixture Distribution is introduced into one kind of inhomogeneous Hidden Markov model-simplified Duration Distribution Based HMM in this paper.
根据纹理特征的局部马尔可夫性和高斯变量的条件回归之间的关系，将复杂的模型选择转变为较简单的变量选择，应用惩罚正则化技巧同步选择邻域和估计参数。
The structure of the GGM is explored by the connection between the local Markov property of texture features and the conditional regression of Gaussian random variables.
广义高斯—马尔可夫模型是平差理论的一般模型。
The Generalized Gauss-Markoff model is the mast general form of adjustment model.
在图像的多分辨率小波分析的基础上 ，采用高斯 -马尔可夫随机场模型来描述图像的局部特征 。
The hierarchical multiresolution wavelet analysis in conjunction with the contextual information of the image extracted from GMRF results in local features of the image.
在图像的多分辨率小波分析的基础上 ，采用高斯 -马尔可夫随机场模型来描述图像的局部特征 。
The hierarchical multiresolution wavelet analysis in conjunction with the contextual information of the image extracted from GMRF results in local features of the image.
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