Abstract
Cutting out an object and estimating its opacity mask, known as image matting, is a key task in image and video editing. Due to the highly ill-posed issue, additional inputs, typically user-defined trimaps or scribbles, are usually needed to reduce the uncertainty. Although effective, it is either time consuming or only suitable for experienced users who know where to place the strokes. In this work, we propose a decomposed-uncertainty-guided matting (dugMatting) algorithm, which explores the explicitly decomposed uncertainties to efficiently and effectively improve the results. Basing on the characteristic of these uncertainties, the epistemic uncertainty is reduced in the process of guiding interaction (which introduces prior knowledge), while the aleatoric uncertainty is reduced in modeling data distribution (which introduces statistics for both data and possible noise). The proposed matting framework relieves the requirement for users to determine the interaction areas by using simple and efficient labeling. Extensively quantitative and qualitative results validate that the proposed method significantly improves the original matting algorithms in terms of both efficiency and efficacy.
Abstract (translated)
去除物体并估计其透明度蒙皮,也称为图像剪辑,是图像和视频编辑中的关键任务。由于存在高度不兼容的问题,通常需要用户定义的Trimap或涂鸦等额外的输入来减少不确定性。虽然有效,但它要么需要时间,要么只适用于有经验的用户,知道在哪里画线。在本工作中,我们提出了一种分解不确定性引导剪辑(dug Matting)算法,该算法 explicitly分解不确定性以有效地和有效地改进结果。基于这些不确定性的特征,在指导相互作用的过程中,知识不确定性减少(引入先前知识),而在建模数据分布的过程中, aleatoric不确定性减少(引入数据和可能噪声的统计数据)。提出的剪辑框架解除了用户通过简单高效的标签来确定相互作用区域的 requirement。广泛的定量和定性结果证明了 proposed 方法在效率和效果方面都显著改进了原始的剪辑算法。
URL
https://arxiv.org/abs/2306.01452
