A Subspace Approach to Layer Extraction

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A Subspace Approach to Layer Extraction
Representing images with layers has many important applications, such as video compression, motion analysis, and 3D scene analysis. This paper presents an 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. Layers in the input images will be mapped in the subspace, where it is proven that they form well-defined clusters and can be reliably identified by a simple mean-shift based clustering algorithm. Global optimality is achieved since all valid regions are simultaneously taken into account, and noise can be effectively reduced by enforcing the subspace constraint. Good layer descriptions are shown to be extracted in the experimental results.
Qifa Ke, Takeo Kanade
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2001
Where CVPR
Authors Qifa Ke, Takeo Kanade
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