论文标题
背景垫子
Background Matting
论文作者
论文摘要
当前的最先进的Alpha Matting方法主要依赖于三位一体作为估计α的次要和唯一的指导。本文研究了在α计算过程中利用背景信息以及构图的影响。为了实现这一目标，采用并修改了Alphagan，以将背景信息作为额外的输入通道处理。进行了广泛的实验，以分析图像和视频垫子中背景信息的效果，例如具有轻度和严重扭曲的背景的训练。基于对Adobe组成1K数据集进行的定量评估，所提出的管道使用字母标准指标明显优于最先进的方法。
The current state of the art alpha matting methods mainly rely on the trimap as the secondary and only guidance to estimate alpha. This paper investigates the effects of utilising the background information as well as trimap in the process of alpha calculation. To achieve this goal, a state of the art method, AlphaGan is adopted and modified to process the background information as an extra input channel. Extensive experiments are performed to analyse the effect of the background information in image and video matting such as training with mildly and heavily distorted backgrounds. Based on the quantitative evaluations performed on Adobe Composition-1k dataset, the proposed pipeline significantly outperforms the state of the art methods using AlphaMatting benchmark metrics.
