CLOUD IMAGE DETECTION BASED ON MARKOV RANDOM FIELD  

作　　者：Xu Xuemei Guo Yuanwei Wang Zhenfei 

机构地区：[1]School of Physics and Electronics,Central South University,Changsha 410083,China

出　　处：《Journal of Electronics(China)》2012年第3期262-270,共9页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No. 61172047)

摘　　要：In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field （MRF） modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation （BP） algorithm and Iterated Conditional Modes （ICM） algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.

关 键 词：Cloud image detection Markov Random Field (MRF) Belief Propagation (BP) Iterated Conditional Modes (ICM) 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]