Bayesian Estimation of Layers from Multiple Images

When estimating foreground and background layers (or equivalently an alpha matte), it is often the case that pixel measurements contain mixed colours which are a combination of foreground and background. Object boundaries, especially at thin sub-pixel structures like hair, pose a serious problem. In this paper we present a multiple view algorithm for computing the alpha matte. Using a Bayesian framework, we model each pixel as a combined sample from the foreground and background and compute a MAP estimate to factor the two. The novelties in this work include the incorporation of three different types of priors for enhancing the results in problematic scenes. The priors used are inequality constraints on colour and alpha values, spatial continuity, and the probability distribution of alpha values. The combination of these priors result in accurate and visually satisfying estimates. We demonstrate the method on real image sequences with varying degrees of geometric and photometric comple...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Yonatan Wexler, Andrew W. Fitzgibbon, Andrew Zisserman
• Keywords: Alpha Matte, Alpha Values, Background Layers, Computer Vision, ECCV 2002, Multiple View Algorithm, Real Image Sequences