Unsupervised image segmentation using Markov Random Fields

被引：0
|
作者：
Sengur, Abdulkadir [1 ]
Turkoglu, Ibrahim
Ince, M. Cevdet
机构：
[1] Firat Univ, Dept Elect & Comp Sci, TR-23119 Elazig, Turkey
[2] Firat Univ, Dept Elect Elect Engn, TR-23119 Elazig, Turkey
来源：
ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS | 2006年 / 3949卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this study, we carried out an unsupervised gray level image segmentation based on Markov Random Fields (MRF) model. First, we use the Expectation Maximization (EM) algorithm to estimate the distribution of the input image and the number of the components is automatically determined by the Minimum Message Length (MML) algorithm. Then the segmentation is done by the Iterated Conditional Modes (ICM) algorithm. For testing the segmentation performance, we use both artificial images and real images. The experimental results are satisfactory.
引用
收藏
页码：158 / 167
页数：10
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