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Multi-scale foreground extraction on graph cut

机译:图割的多尺度前景提取

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In order to improve Grab Cut implementation effect for real images, we propose a novel improvement which extends the Grab Cut in three aspects: 1) a series of edge-preserved components are generated via the TV smoothing model; 2) the number of sub-regions is estimated by histogram shape analysis to remove the negative effects on the unreasonable number of the sub-regions; 3) a segmentation termination condition is constructed by integrating the multi-scale components. The experiment result indicates that this method performs well compared to other methods based on graph cut and is insensitive to sub-regions.
机译:为了提高对真实图像的Grab Cut实现效果,我们提出了一种新颖的改进,它从三个方面扩展了Grab Cut:1)通过电视平滑模型生成一系列边缘保留的分量; 2)通过直方图形状分析估计子区域的数量,以消除对子区域的不合理数量的负面影响; 3)通过集成多尺度分量来构造分割终止条件。实验结果表明,与基于图割的其他方法相比,该方法表现良好,并且对子区域不敏感。

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