文中通过利用马尔可夫随机场模型，引入图像象素的局部结构信息，有效实现了SAR目标切片图像的高精度分割。
Utilizing MRF (Markov Random Field) model to introduce the pixel's local context information, a quite accurate segmentation of SAR target chip image is realized.
研究了基于隐马尔可夫随机场（HMRF）模型的无监督图像分割问题。
This paper presents a novel unsupervised image segmentation algorithm based on hidden Markov random field(HMRF) model.
本文提出了一种用于图像分割的分层随机场模型。
This paper proposes a hierarchical random field model for segmentation of images.
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.
针对目标监测分析中的SAR图像分割问题，构造了一种基于马尔可夫随机场(MRF)模型和形态学运算的处理方法。
A combined method based on Markov Random Field (MRF) model and morphological operation was presented for the segmentation of the SAR image in target monitoring.
方法：根据马尔科夫随机场图像模型，利用最大后验概率准则(MA P)，提出一种迭代松弛分割算法。
Methods: Based on Markov random fields model of noise, a iteration algorithm was presented by using maximum a posteriori (MAP) criterion.
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.
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.
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