根据条件随机场模型和马尔可夫随机场模型建立了一个最大后验概率框架。
Firstly, maximum a posteriori framework is created according to conditional random field model and Markov random field 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.
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.
在许多采用马尔可夫随机场模型的基于样图的纹理合成算法中，邻域的大小决定着这些合成算法的纹理合成质量和合成速度。
For many sample-based texture synthesis algorithms using markov random field model, the size of neighborhood determines the quality and speed of texture synthesis.
基于图像在小波域的马尔可夫随机场模型(MRF)结构，结合SAR图像中相干斑噪声的统计特性，本文提出了一种新的小波域相干斑抑制方法。
Integrating the statistical characteristics of speckle noise in SAR images with wavelet-domain Markov random field (MRF) structure of images, a new wavelet-domain spec.
针对遥感图像分布的不均匀性，该文提出的算法没有采用固定的马尔可夫随机场模型参数，而是在递归分类算法中分级地调整模型参数以适应区域的变化。
As the MRF model with fixed parameters does not fit the real remotely sensed image very well, this paper adjusts the MRF model "s parameters during the classification procedure."
针对遥感图像分布的不均匀特性，该文提出的算法没有采用固定的马尔可夫随机场模型参数，而是在递归分类算法中分级地调整模型参数以适应区域的变化。
As the MRF model with fixed parameters does not fit the real remotely sensed image very well, this paper adjusts the MRF model's parameters during the classification procedure.
条件随机场是一种无向图模型，它具有产生式模型和最大熵马尔可夫模型的优点。
Conditional Random Fields (CRF) is arbitrary undirected graphical model that bring together the best of generative models and Maximum Entropy Markov models (MEMM).
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.
讨论了基于马尔可夫随机场(MRF)模型的融合颜色和边缘信息的嘴唇特征提取方法。
This paper introduced a MRF (Markov Random Field) -based method of integrating color and spatial edge information to address the problem of lip feature extraction.
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.
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.
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