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A Robust Subspace Approach to Layer Extraction (2002)
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BibTeX
@MISC{Ke02arobust,
author = {Qifa Ke and Takeo Kanade},
title = {A Robust Subspace Approach to Layer Extraction},
year = {2002}
}
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Abstract
Representing images with layers has many important applications, such as video compression, motion analysis, and 3D scene analysis. This paper presents a robust subspace approach to reliably extracting layers from images by taking advantages of the fact that homographies induced by planar patches in the scene form a low dimensional linear subspace. Such 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.
Keyphrases
robust subspace approach layer extraction feature space video compression motion analysis many important application good layer description local measurement algorithm robust temporal redundancy subspace constraint multiple frame scene analysis image domain low dimensional linear subspace planar patch experimental result better-defined cluster
