Segmentation of textured images using a multiresolution Gaussian autoregressive model
Segmentation of textured images using a multiresolution Gaussian autoregressive model
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DOI：
10.1109/83.748895
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发表时间：
1999-03-01
影响因子：
10.6
通讯作者：
Delp, EJ
中科院分区：
文献类型：
--
作者：
Comer, ML;Delp, EJ
我们提出了一种使用多分辨率贝叶斯方法进行纹理图像分割的新算法。新算法使用多分辨率高斯自回归（MGAR）模型的金字塔表示的观察到的图像，并假设一个多尺度马尔可夫随机场模型的类标签金字塔。与先前提出的贝叶斯多分辨率分割方法不同，这些方法要么使用观察图像的单分辨率表示，要么隐含地假设观察图像的多分辨率表示的不同级别之间的独立性，本文中使用的模型结合了观察图像金字塔和类别标签金字塔的不同层次之间的相关性。用于分割的标准是最小化多分辨率格中误分类节点数的期望值。满足这一标准的估计被称为“多分辨率最大化的后边缘”（MMPM）估计，是一个自然的扩展单分辨率“最大化的后边缘”（MPM）估计。以前的多分辨率分割技术已经被证明是基于最大后验概率（MAP）估计准则，这已被证明是不太适合分割比MPM criterion.It假设，在观察到的图像中的不同纹理的数量是已知的，MGAR模型的参数的均值，预测系数，和预测误差方差的不同的纹理是未知的。ii修改版本的期望最大化（EM）算法被用来估计这些参数。假设标签金字塔的吉布斯分布的参数是已知的。实验结果表明该算法的性能。
We present a new algorithm for segmentation of textured images using a multiresolution Bayesian approach. The new algorithm uses a multiresolution Gaussian autoregressive (MGAR) model for the pyramid representation of the observed image, and assumes a multiscale Markov random field model for the class label pyramid. Unlike previously proposed Bayesian multiresolution segmentation approaches, which have either used a single-resolution representation of the observed image or implicitly assumed independence between different levels of a multiresolution representation of the observed image, the models used in this paper incorporate correlations between different levels of both the observed image pyramid and the class label pyramid.The criterion used for segmentation is the minimization of the expected value of the number of misclassified nodes in the multiresolution lattice. The estimate which satisfies this criterion is referred to as the "muitiresolution maximization of the posterior marginals" (MMPM) estimate, and is a natural extension of the single-resolution "maximization of the posterior marginals" (MPM) estimate. Previous multiresolution segmentation techniques have been based on the maximum a posteriori (MAP) estimation criterion, which has been shown to be less appropriate for segmentation than the MPM criterion.It is assumed that the number of distinct textures in the observed image is known, The parameters of the MGAR model-the means, prediction coefficients, and prediction error variances of the different textures-are unknown. ii modified version of the expectation-maximization (EM) algorithm is used to estimate these parameters. The parameters of the Gibbs distribution for the label pyramid are assumed to be known. Experimental results demonstrating the performance of the algorithm are presented.