This paper proposes a hierarchical random field model for segmentation of images.
本文提出了一种用于图像分割的分层随机场模型。
Firstly, maximum a posteriori framework is created according to conditional random field model and Markov random field model.
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。
For many sample-based texture synthesis algorithms using markov random field model, the size of neighborhood determines the quality and speed of texture synthesis.
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。
In this paper, a kind of generalized multiscale fractal parameter is proposed and constructed for the description of image textures, which is based on fractional Brownian random field model.
本文从分数布朗随机场模型的描述出发，提出并构造了一种广义多尺度分形参数，用于描述图像的纹理信息。
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.
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.
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。
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