Maximum a posteriori blind image deconvolution with Huber–Markov random-field regularization

2009 ◽  
Vol 34 (9) ◽  
pp. 1453 ◽  
Author(s):  
Zhimin Xu ◽  
Edmund Y. Lam
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Maximum A Posteriori ◽  
Image Deconvolution ◽  
A Posteriori ◽  
Markov Random ◽  
Blind Image Deconvolution ◽  
Blind Image

Vision Monitoring Algorithm for Work Condition of NC Processing

2009 ◽  
Vol 16-19 ◽  
pp. 920-924
Author(s):  
Yuan Zhang ◽  
Shao Chen Kang ◽  
Peng Wang ◽  
Shi Wei Yin
Keyword(s):  
Markov Random Field ◽  
Video Analysis ◽  
High Speed ◽  
Image Sequence ◽  
Maximum A Posteriori ◽  
Work Piece ◽  
A Posteriori ◽  
Work Condition ◽  
Markov Random ◽  
Monitoring Algorithm

Aim at the problem of work condition monitoring in high speed NC processing, a new method is proposed based on video analysis technology. It puts forward a segmentation model with maximum a posteriori and Markov random field for NC processing image sequence. Moreover, the best segmentation is obtained by optimization immunity algorithm to monitor tool and work piece effectively. The experiment shows that this method can achieve accurate and real time monitoring and demonstrate the validity and robust of the solution for barrier work piece.

Fusion Moves for Markov Random Field Optimization

2010 ◽  
Vol 32 (8) ◽  
pp. 1392-1405 ◽  
Author(s):  
Victor Lempitsky ◽  
Carsten Rother ◽  
Stefan Roth ◽  
Andrew Blake
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Markov Random
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