An image change detection algorithm based on Markov random field models

2002 ◽  
Vol 40 (8) ◽  
pp. 1815-1823 ◽  
Author(s):  
T. Kasetkasem ◽  
P.K. Varshney
Keyword(s):  
Change Detection ◽  
Random Field ◽  
Markov Random Field ◽  
Detection Algorithm ◽  
Markov Random ◽  
Image Change Detection

Change Detection Algorithm on Wavelet and Markov Random Field

2015 ◽  
Vol 8 (4) ◽  
pp. 181-190 ◽  
Author(s):  
Hongxun Song ◽  
Weixing Wang ◽  
Tingting Zhang ◽  
Tianchao Yu ◽  
Junfang Song
Keyword(s):  
Change Detection ◽  
Random Field ◽  
Markov Random Field ◽  
Detection Algorithm ◽  
Markov Random

scholarly journals SAR IMAGE CHANGE DETECTION BASED ON FUZZY MARKOV RANDOM FIELD MODEL

2018 ◽  
Vol XLII-3 ◽  
pp. 2371-2374
Author(s):  
J. Zhao ◽  
G. Huang ◽  
Z. Zhao
Keyword(s):  
Change Detection ◽  
Random Field ◽  
Markov Random Field ◽  
Fuzzy Theory ◽  
Detection Accuracy ◽  
Sar Image ◽  
Markov Random ◽  
Image Change Detection ◽  
Image Pixels ◽  
Detection Effect

Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels. So the change detection results are susceptible to image noise, and the detection effect is not ideal. Markov Random Field (MRF) can make full use of the spatial dependence of image pixels and improve detection accuracy. When segmenting the difference image, different categories of regions have a high degree of similarity at the junction of them. It is difficult to clearly distinguish the labels of the pixels near the boundaries of the judgment area. In the traditional MRF method, each pixel is given a hard label during iteration. So MRF is a hard decision in the process, and it will cause loss of information. This paper applies the combination of fuzzy theory and MRF to the change detection of SAR images. The experimental results show that the proposed method has better detection effect than the traditional MRF method.

Multi-shape Infrared Target Detection Algorithm Based on Markov Random Field

2013 ◽  
Vol 42 (10) ◽  
pp. 1231-1237
Author(s):  
薛永宏 XUE Yong-hong ◽  
张涛 ZHANG Tao ◽  
陈荣利 CHEN Rong-li ◽  
安玮 AN Wei ◽  
张寅生 ZHANG Yin-sheng
Keyword(s):  
Random Field ◽  
Target Detection ◽  
Markov Random Field ◽  
Detection Algorithm ◽  
Markov Random
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