多变量信号贝叶斯数据融合的隐马尔可夫模型
A Hidden Markov model for Bayesian data fusion of multivariate signals
摘要：
在这项工作中，我们提出了一个贝叶斯框架的多变量信号的数据融合成像系统中出现的。更具体地说，我们考虑的情况下，我们已经通过两个不同的成像过程观察到同一对象的两个图像。这项工作的目的是，然后提出一个连贯的方法来结合这些数据集，以获得一个分割的图像，可以被认为是这两个图像的融合结果。所提出的方法是基于一个隐马尔可夫模型（HMM）的图像与共同的分割，或等效的，与共同的隐藏的分类标签变量，这是由波茨马尔可夫随机场建模。然后，我们提出了一个适当的马尔可夫链蒙特卡罗（MCMC）算法来实现的方法，并显示一些模拟结果和应用。
英文摘要：
In this work we propose a Bayesian framework for data fusion of multivariate signals which arises in imaging systems. More specifically, we consider the case where we have observed two images of the same object through two different imaging processes. The objective of this work is then to propose a coherent approach to combine these data sets to obtain a segmented image which can be considered as the fusion result of these two images. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently, with common hidden classification label variables which is modeled by the Potts Markov Random Field. We propose then an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results and applications.
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