scholarly journals Hierarchical conditional random field model for multi‐object segmentation in gastric histopathology images

2020 ◽  
Vol 56 (15) ◽  
pp. 750-753
Author(s):  
Changhao Sun ◽  
Chen Li ◽  
Jinghua Zhang ◽  
Frank Kulwa ◽  
Xiaoyan Li
Keyword(s):  
Random Field ◽  
Field Model ◽  
Object Segmentation ◽  
Conditional Random Field ◽  
Random Field Model ◽  
Conditional Random Field Model ◽  
Histopathology Images

scholarly journals Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yibo Li ◽  
Yuxiang Zhang ◽  
Huiyu Zhu ◽  
Rongxin Yan ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Random Field ◽  
Storage Tank ◽  
Field Model ◽  
Conditional Random Field ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Random Field Model ◽  
Conditional Random Field Model

Acoustic emission (AE) technique is often used to detect inaccessible area of large storage tank floor with AE sensors placed outside the tank. For tanks with fixed roofs, the drop-back signals caused by condensation mix with corrosion signals from the tank floor and interfere with the online AE inspection. The drop-back signals are very difficult to filter out using conventional methods. To solve this problem, a novel AE inner detector, which works inside the storage tank, is adopted and a pattern recognition algorithm based on CRF (Conditional Random Field) model is presented. The algorithm is applied to differentiate the corrosion signals from interference signals, especially drop-back signals caused by condensation. Q235 steel corrosion signals and drop-signals were collected both in laboratory and in field site, and seven typical AE features based on hits and frequency are extracted and selected by mRMR (Minimum Redundancy Maximum Relevance) for pattern recognition. To validate the effectiveness of the proposed algorithm, the recognition result of CRF model was compared with BP (Back Propagation), SVM (Support Vector Machine), and HMM (Hidden Markov Model). The results show that training speed, accuracy, and ROC (Receiver Operating Characteristic) results of the CRF model outperform other methods.

scholarly journals Linear street extraction using a Conditional Random Field model

2015 ◽  
Vol 14 ◽  
pp. 532-545 ◽  
Author(s):  
Padraig Corcoran ◽  
Peter Mooney ◽  
Michela Bertolotto
Keyword(s):  
Random Field ◽  
Field Model ◽  
Conditional Random Field ◽  
Random Field Model ◽  
Conditional Random Field Model
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