Fast algorithms of Bayesian Segmentation of Images
Fast algorithms of Bayesian Segmentation of Images
复制标题
图像贝叶斯分割的快速算法
DOI：
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复制
发表时间：
2002
期刊：
影响因子：
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通讯作者：
B. Zalesky
中科院分区：
文献类型：
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作者：
B. Zalesky
针对灰度图像和彩色图像的贝叶斯分割，提出了网络流优化方法。假设图像像素由取有限个任意有理值（可以是图像的强度值或其他特征）的特征函数来表征。同质像素的聚类由具有另一组有理数中的值的标签来描述。假设它们是相关的，并根据指数或高斯吉布斯定律分布。代替传统上使用的最近像素的局部邻域，所有像素的依赖的完全连接图被用于吉布斯先验分布。开发的方法减少了分割的问题，以确定一个适当的网络的最小切割的问题。
The network flow optimization approach is offered for Bayesian segmentation of gray-scale and color images. It is supposed image pixels are characterized by a feature function taking finite number of arbitrary rational values (it can be either intensity values or other characteristics of images). The clusters of homogeneous pixels are described by labels with values in another set of rational numbers. They are assumed to be dependent and distributed according to either the exponential or the Gaussian Gibbs law. Instead traditionally used local neighborhoods of nearest pixels the completely connected graph of dependence of all pixels is employed for the Gibbs prior distributions. The methods developed reduce the problem of segmentation to the problem of determination of the minimum cut of an appropriate network.
DOI：
10.1109/tpami.1984.4767596
发表时间：
1984-01-01
影响因子：
23.6
作者：
GEMAN, S;GEMAN, D
通讯作者：
GEMAN, D