w惩罚及其在带稀疏标签的Alpha抠图中的应用

Stephen Tierney, Junbin Gao, Yi Guo
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引用次数: 0

摘要

Alpha抠图是一个病态问题,因此用户必须提供密集的部分标签才能得到一个可接受的解决方案。不幸的是,这种标签可能很耗时。在本文中,我们引入了w惩罚函数,当将其与现有的抠图技术结合时,用户可以提供极其稀疏的输入。制定的目标函数鼓励将哑光值驱动到0和1。实验表明,该模型优于目前最先进的KNN抠图算法。我们提出的方法的MATLAB代码可以在MatteKit包中免费获得。
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The W-Penalty and Its Application to Alpha Matting with Sparse Labels
Alpha matting is an ill-posed problem, as such the user must supply dense partial labels for an acceptable solution to be reached. Unfortunately this labelling can be time consuming. In this paper we introduce the w-penalty function, which when incorporated into existing matting techniques allows users to supply extremely sparse input. The formulated objective function encourages driving matte values to 0 and 1. The experiments demonstrate the proposed model outperforms the state-of-the-art KNN matting algorithm. MATLAB code for our proposed method is freely available in the MatteKit package.
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