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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
Dynamic Graph Cuts in Parallel
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| 英文论文题目: |
Dynamic Graph Cuts in Parallel
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| 论文题目英文: |
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| 作者: |
Yu, Miao
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| 论文出处: |
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| 刊物名称: |
IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 年: |
2017
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| 卷: |
26 |
| 期: |
8 |
| 页: |
3775-3788 |
| 联系作者: |
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| 收录类别: |
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| 影响因子: |
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| 摘要: |
This paper aims at bridging the two important trends in efficient graph cuts in the literature, the one is to decompose a graph into several smaller subgraphs to take the advantage of parallel computation, the other is to reuse the solution of the max-flow problem on a residual graph to boost the efficiency on another similar graph. Our proposed parallel dynamic graph cuts algorithm takes the advantages of both, and is extremely efficient for certain dynamically changing MRF models in computer vision. The performance of our proposed algorithm is validated on two typical dynamic graph cuts problems: the foreground-background segmentation in video, where similar graph cuts problems need to be solved in sequential and GrabCut, where graph cuts are used iteratively. |
| 英文摘要: |
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