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markov random field in matlab code-markov random field in Matlab code
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一个马尔可夫（MRF）图像分割MATLAB的源码,有30几个函数。Markov随机场的例子程序,对于初学入门MRF的人很有用,能得到直观的印象。-A Markov (MRF) image segmentation MATLAB source code, 30 a few functions. Markov random sample program, for beginners who are useful entry-MRF can get a visual impression.
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一种基于马尔科夫随机场的图像分割matlab源码，包含ICM迭代条件模式求解最大后验概率算法，已通过测试。-Markov random field based image segmentation matlab source code, including the ICM iteration conditions for solving the maximum a posteriori probability model algorithm has been tested.
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提出了一种基于函数联接的感知器神经网络的纹理分类方法.它采用高斯2马尔柯夫随机场模型(GM RF)对纹理进行描述,模型参数即为纹理特征,参数估计采用最小平方误差方法获得.将估计参数作为表达纹理的特征向量,用感知器网络对特征进行分类,并且采用函数联接的方式解决线性不可分问题.对纹理图象进行的实验表明,采用这种方法能够提高学习速度,简化计算过程,并取得较好的纹理分类效果.
-Based on the function connected perceptron neural network tex
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Matlab code for encoding an unwrapping phase InSAR image based on Markov Random field
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MRF的例子程序,该程序用Matlab进行编写的，对图像进行MRF的处理，可以用于分割分类等-markov random field
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针对合成孔径雷达(SAR) 图像含有大量斑点噪声的特点,基于Contourlet 的多尺度、局部化、方向性和各向
异性等优点,并结合隐马尔科夫树( HMT) 模型和隐马尔科夫场(MRF) ,提出了一种基于Contourlet 域持续性和聚
集性的SAR 图像模糊融合分割算法。该算法有效捕获了Contourlet 子带的持续性和聚集性,并分别用HMT 和
MRF 来刻画,再依据模糊测度,将多尺度HMT 和MRF 有机融合,建立Contourlet 域HMT2MRF 融合模型,并导
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Markov Random Field: Theory and Application
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This the sample implementation of a Markov random field (MRF) based
image segmentation algorithm-This is the sample implementation of a Markov random field (MRF) based
image segmentation algorithm
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In this paper, human skin detection is performed using a new color space coordinate and a Markov random fi eld based approach.
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ICM_Algorithm Markov Random Field
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for markov random field
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这是一个马尔科夫随机场的代码，能够轻松的测试程序效果-This is the code of a Markov random field can easily test program effect
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吉布斯分布代码，常用于马尔科夫随机场分割和识别-Gibbs distribution code, commonly used for Markov Random Field segmentation and recognition
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Markov random field, belief propagation
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压缩感知中置信传播和马尔可夫随机场的全部源代码-Compressed sensing belief propagation and Markov random field full source code
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A non-parametric method for texture synthesis proposed.
The texture synthesis process grows a new image
outward from an initial seed, one pixel at a time. A Markov
random field model is assumed, and the conditional distribution
of a pixel giv
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Most recent approaches have posed texture synthesis in a
statistical setting as a problem of sampling from a probability
distribution. Zhu et. al. [12] model texture as a Markov
Random Field and use Gibbs sampling for synthesis. Unfortunately,
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将置信传播(belief propagation,BP)算法从马尔科夫随机域的角度进行理解，
并通过变量节点和校验界定之间的迭代来实现信息传递，进而提高系统的误码率性能。
-The belief propagation (belief propagation, BP) algorithm is understood from the perspective of Markov random field, and by defining the iteration variable nod
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置信传播算法可通过因子图的角度理解，也可通过马尔科夫随机域的思想来理解，不管从哪个角度实现，都可将其应用于检测，提高性能。-Belief propagation algorithm can be understood by the angle factor graph can also be understood by thinking of Markov random field, regardless of the angle from which to achieve, can be ap
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