Recursive Least Squares Parameter Estimation for Gaussian Markov Random Field Model

Zou Cairong; He Zhenya; Wang Taijun

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Recursive Least Squares Parameter Estimation for Gaussian Markov Random Field Model

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- Recursive Least Squares Parameter Estimation for Gaussian Markov Random Field Model
- Acta Electronica Sinica Issue 4, Pages: 89-92(1992)
- 作者机构：
  
  1. 东南大学无线电系
  2. 东南大学无线电系南京 210018
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：1992
- 稿件说明：
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[1]邹采荣,何振亚,王太君.高斯马尔可夫随机场模型的递归最小二乘参数估计[J].电子学报,1992(04):89-92.

Zou Cairong, He Zhenya, Wang Taijun. Recursive Least Squares Parameter Estimation for Gaussian Markov Random Field Model[J]. Acta Electronica Sinica, 1992, (4): 89-92.
[1]邹采荣,何振亚,王太君.高斯马尔可夫随机场模型的递归最小二乘参数估计[J].电子学报,1992(04):89-92. DOI：

Zou Cairong, He Zhenya, Wang Taijun. Recursive Least Squares Parameter Estimation for Gaussian Markov Random Field Model[J]. Acta Electronica Sinica, 1992, (4): 89-92. DOI：

摘要

本文首次提出应用阶递归最小二乘算法来估计高斯马尔可夫随机场模型参数。利用模型参数关于领域的对称性质

我们将一个非因果对称邻域支持的高斯马尔可夫随机场模型转化成一个因果非对称半平面部域支持的模型

从而使递归计算成为可能。利用规范方程中系数矩阵的近似Toeplitz性质

导出了运算量为O（m

）+O（M

m）MADP的阶递归最小二乘算法

而直接采用解方程法的计算量为O（m

）+O（M

）

这里M

表示一幅图象的尺寸

m代表模型参数的个数。

Abstract

This paper

for the first time

presents an order recursive least squares（LS） algorithm for the parameter estimation of Gaussian Markov Random Field （GMRF） model. The symmetric property of the parameters about the neighbor set is used to change the noncausal GMRF model into a causal nonsym metric half-plane （NSHP） supported model. Based on the approximate Toeplitz structure of the coefficient matrix in the normal equation

we derive an order recursive LS algorithm with the computation complexity of O（m3） + O（M2m）MADP

While the direct LS method needs the computation complexity of O（m3） + O（M2m2）

where M2 represents the size of an image and m is the total number of parameters to be estimated.

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