This paper proposes a Maxkov Random Field （MRF） model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several...This paper proposes a Maxkov Random Field （MRF） model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several joint sub-regions. In each sub-region, we partition the colors of neighboring background or foreground pixels into several clusters in RGB color space and assign matting label to each unknown pixel. All the labels are modelled as an MRF and the matting problem is then formulated as a maximum a posteriori （MAP） estimation problem. Simulated annealing is used to find the optimal MAP estimation. The better results can be obtained under the same user-interactions when images are complex. Results of natural image matting experiments performed on complex images using this approach are shown and compared in this paper.展开更多
为了降低自然图像抠图中抠图算法对用户输入的敏感度,提出了规范化用户输入空间(Normalized User Input Space,NUIS)的概念及一种基于NUIS空间的抠图算法(NUIS-Matting)。方法首先将原始图像过分割为超像素(superpixels)并引入超像素前...为了降低自然图像抠图中抠图算法对用户输入的敏感度,提出了规范化用户输入空间(Normalized User Input Space,NUIS)的概念及一种基于NUIS空间的抠图算法(NUIS-Matting)。方法首先将原始图像过分割为超像素(superpixels)并引入超像素前景不透明度以提高算法的抗噪能力,再用超像素构造NUIS空间并将原始用户输入映射到NUIS空间。然后使用一种更有效的采样方法在NUIS空间中采样前景及背景颜色对来计算未知区域像素点的前景不透明度及其置信度,并选取对应高置信度的不透明度作为初始结果;最后使用随机游走(random walk)解一个图标记问题(graph labeling problem)得出优化后的结果。实验结果表明,方法大大降低了抠图对用户输入的敏感度,提高了抠图结果的质量。展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant No. 600330107 Zhejiang Provincial Natural Science Foundation of China under Grant No, Y105324 and Planned Program of Science and Technology Department of Zhejiang Province, China （Grant No. 2006C31065）,
文摘This paper proposes a Maxkov Random Field （MRF） model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several joint sub-regions. In each sub-region, we partition the colors of neighboring background or foreground pixels into several clusters in RGB color space and assign matting label to each unknown pixel. All the labels are modelled as an MRF and the matting problem is then formulated as a maximum a posteriori （MAP） estimation problem. Simulated annealing is used to find the optimal MAP estimation. The better results can be obtained under the same user-interactions when images are complex. Results of natural image matting experiments performed on complex images using this approach are shown and compared in this paper.
