Markov Chain / Transition Probability Approach for Geo-Cellular Modeling of Carbonate Depositional Geobodies: Proof-of-Concept Markov Random Field Simulator

2011 ◽  
Author(s):  
Brigitte Vlaswinkel ◽  
Sam Purkis ◽  
Nuno Gracias
Keyword(s):  
Markov Chain ◽  
Random Field ◽  
Markov Random Field ◽  
Transition Probability ◽  
Proof Of Concept ◽  
Probability Approach ◽  
Markov Random ◽  
Cellular Modeling

Bilayer Markov Random Field Method for Detecting Defects in Patterned Fabric

2021 ◽  
Author(s):  
Xingzhi Chang ◽  
Wei Liu ◽  
Chuan Zhu ◽  
Xiaohua Zou ◽  
Guan Gui
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Transition Probability ◽  
Estimation Method ◽  
Ground Truth ◽  
Recall Rate ◽  
Field Method ◽  
Markov Random ◽  
Block Level ◽  
Data Layer

Existing block-level defect detection method in patterned fabric causes a large number of false detections due to the lack of edge information. To solve this problem, in this paper, we propose a bilayer Markov random field (BMRF) method for inspecting defects in patterned fabric. First, the proposed method reduces samples of the original fabric image to obtain the constraint layer, which can locate the defective block roughly. Second, we interpolate samples into the image to supplement the local information to improve and optimize the imperfect boundary, to obtain a more detailed data layer. Moreover, this paper proposes a new potential function, which considers the differential characteristics of the image blocks in the same layer and the transition probability between different layers. Finally, this paper utilizes a parameter estimation method based on the expectation maximization to solve the parameters of the BMRF method. The proposed BMRF method is evaluated on databases of star-, box- and dot-patterned fabrics. By comparing the resultant and ground-truth images, the recall rate of the proposed method in the three patterned fabrics is 95.32%, 89.29% and 93.28%, respectively, which is comparable to the existing methods.

Using Local Transition Probability Models in Markov Random Field for Multi-Temporal Image Classification

2014 ◽  
Vol 687-691 ◽  
pp. 3963-3967
Author(s):  
Wu Ping Liu ◽  
Fu Wei
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Classification Accuracy ◽  
Poyang Lake ◽  
Transition Probability ◽  
Single Image ◽  
Probability Models ◽  
Markov Random ◽  
Multi Temporal ◽  
Local Transition

Making use full of multi-source and multi-temporal information to extract richer and interesting information is a tendency in analysis of remote sensing images. In this paper, spatial and temporal contextual classification based on Markov Random Field (MRF) is used to classify ecological function vegetation in Poyang Lake. The results show that spatial and temporal neighborhood complementary information from different images can be used to remove the spectral confusion of different kinds of vegetation on single image and improve classification accuracy compared to MLC method. Building effective spatial and temporal neighborhood model for information extraction in special application is the key of multi-source and multi-temporal image analysis. Although spatial and temporal contextual classification method is computation demanding, it’s promising in the application emphasizing classification accuracy.

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