Foreground color prediction through inverse compositing

被引:0
|
作者
Lutz, Sebastian [1 ]
Smolic, Aljosa [1 ]
机构
[1] Trinity Coll Dublin, V SENSE, Dublin, Ireland
来源
2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2021) | 2021年
基金
爱尔兰科学基金会;
关键词
D O I
10.1109/WACV48630.2021.00165
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In natural image matting, the goal is to estimate the opacity of the foreground object in the image. This opacity controls the way the foreground and background is blended in transparent regions. In recent years, advances in deep learning have led to many natural image matting algorithms that have achieved outstanding performance in a fully automatic manner. However, most of these algorithms only predict the alpha matte from the image, which is not sufficient to create high-quality compositions. Further, it is not possible to manually interact with these algorithms in any way except by directly changing their input or output. We propose a novel recurrent neural network that can be used as a post-processing method to recover the foreground and background colors of an image, given an initial alpha estimation. Our method outperforms the state-of-the-art in color estimation for natural image matting and show that the recurrent nature of our method allows users to easily change candidate solutions that lead to superior color estimations.
引用
收藏
页码:1609 / 1618
页数:10
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