Fast Matting Using Large Kernel Matting Laplacian Matrices
Abstract
Image matting is of great importance in both computer vision and graphics applications. Most existing state-of-the-art techniques rely on large sparse matrices such as the matting Laplacian. However, solving these linear systems is often time-consuming, which is unfavored for the user interaction. In this paper, we propose a fast method for high quality matting. We first derive an efficient algorithm to solve a large kernel matting Laplacian. A large kernel propagates information more quickly and may improve the matte quality. To further reduce running time, we also use adaptive kernel sizes by a KD-tree trimap segmentation technique. A variety of experiments show that our algorithm provides high quality results and is 5 to 20 times faster than previous methods.
Cite
Text
He et al. "Fast Matting Using Large Kernel Matting Laplacian Matrices." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5539896Markdown
[He et al. "Fast Matting Using Large Kernel Matting Laplacian Matrices." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/he2010cvpr-fast/) doi:10.1109/CVPR.2010.5539896BibTeX
@inproceedings{he2010cvpr-fast,
title = {{Fast Matting Using Large Kernel Matting Laplacian Matrices}},
author = {He, Kaiming and Sun, Jian and Tang, Xiaoou},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2010},
pages = {2165-2172},
doi = {10.1109/CVPR.2010.5539896},
url = {https://mlanthology.org/cvpr/2010/he2010cvpr-fast/}
}