Abstract
Extracting accurate foreground objects from a scene is a fundamental step in creating virtual reality content. However, majority of the professional softwares today still require human interventions, e.g., providing trimaps or labeling key frames. This is not only time consuming, but is also error prone. In this paper, we present a fully automatic algorithm to extract foreground objects. Our solution is based on a newly developed concept called the Multi-Agent Consensus Equilibrium (MACE), a framework which allows us to integrate multiple sources of expertise to produce an overall superior result. Our MACE framework consists of three agents: (1) A new dual layer closed-form matting agent to estimate the foreground mask using the color image and a background image; (2) A background probability estimator using color difference and object segmentation; (3) A total variation minimization agent to control the smoothness of the foreground masks. We show how these agents are constructed, and how their interactions lead to better performance. The algorithm is evaluated by comparing to several state-of-the-art methods. Extensive experimental study shows that the proposed method has less error compared to the competing methods.
Abstract (translated by Google)
URL
http://arxiv.org/abs/1808.08210