scholarly journals Susceptibility artefact correction using dynamic graph cuts: Application to neurosurgery

2014 ◽  
Vol 18 (7) ◽  
pp. 1132-1142 ◽  
Author(s):  
Pankaj Daga ◽  
Tejas Pendse ◽  
Marc Modat ◽  
Mark White ◽  
Laura Mancini ◽  
...  
Keyword(s):  
Graph Cuts ◽  
Dynamic Graph ◽  
Susceptibility Artefact ◽  
Artefact Correction

Susceptibility artefact correction by combining B0 field maps and non-rigid registration using graph cuts

2013 ◽  
Author(s):  
Pankaj Daga ◽  
Marc Modat ◽  
Gavin Winston ◽  
Mark White ◽  
Laura Mancini ◽  
...  
Keyword(s):  
Graph Cuts ◽  
Rigid Registration ◽  
Susceptibility Artefact ◽  
Artefact Correction

Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints

2009 ◽  
Vol 52 (2) ◽  
pp. 252-259 ◽  
Author(s):  
Jia Li ◽  
ChengKai Wan ◽  
DianYong Zhang ◽  
ZhenJiang Miao ◽  
BaoZong Yuan
Keyword(s):  
Random Field ◽  
Motion Capture ◽  
Markov Random Field ◽  
Graph Cuts ◽  
Human Motion ◽  
Dynamic Graph ◽  
Human Motion Capture ◽  
Markov Random

Dynamic Graph Cuts in Parallel

2017 ◽  
Vol 26 (8) ◽  
pp. 3775-3788 ◽  
Author(s):  
Miao Yu ◽  
Shuhan Shen ◽  
Zhanyi Hu
Keyword(s):  
Graph Cuts ◽  
Dynamic Graph

scholarly journals Research on Dynamic Graph Target Tracking Method Fusing the Color Local Entropy

2018 ◽  
Vol 17 ◽  
pp. 02004
Author(s):  
Junchang Zhang ◽  
Chenyang Xia ◽  
Leili Hu ◽  
Yanling Zhou
Keyword(s):  
Graph Matching ◽  
Graph Cuts ◽  
Video Tracking ◽  
Local Entropy ◽  
Tracking Accuracy ◽  
Dynamic Graph ◽  
Tracking Method ◽  
Candidate Target ◽  
Simple Linear Iterative Clustering ◽  
Local Gradient

Focusing on the problems of target deformation, occlusion, background interference and rotation, a robust video tracking method is proposed in this paper, which is based on the superpixels and dynamic graph matching. Firstly, to make the superpixels edge fit better and structure tighter, the local gradient feature is fused into the simple linear iterative clustering (SLIC) method. Secondly, the candidate target superpixels set is generated by Graph Cuts and to obtain more accurate foreground superpixels set, the LASVM classification results are fused into the Graph Cuts energy function. Thirdly, in order to make the proposed tracker more robust, the color local entropy is fused into the diagonal elements of the affinity matrix. Experiment results show that the proposed algorithm has strong robustness and better tracking accuracy.

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