A robust subspace approach to layer extraction

Representing images with layers has many important applications, such as video compression, motion analysis, and 3D scene analysis. The paper presents a robust subspace approach to extracting layers from images reliably by taking advantage of the fact that homographies induced by planar patches in the scene form a low dimensional linear subspace. Such a subspace provides not only a feature space where layers in the image domain are mapped onto denser and better-defined clusters, but also a constraint for detecting outliers in the local measurements, thus making the algorithm robust to outliers. By enforcing the subspace constraint, spatial and temporal redundancy from multiple frames are simultaneously utilized, and noise can be effectively reduced. Good layer descriptions are shown to be extracted in the experimental results.