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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.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Number of pages: 7

Text Reference

Qifa Ke and Takeo Kanade, "A Robust Subspace Approach to Layer Extraction," IEEE Workshop on Motion and Video Computing (Motion'2002), Orlando, Florida, Dec. 2002., December, 2002.

BibTeX Reference

@inproceedings{Ke_2002_4162,
   author = "Qifa Ke and Takeo Kanade",
   title = "A Robust Subspace Approach to Layer Extraction",
   booktitle = "IEEE Workshop on Motion and Video Computing (Motion'2002), Orlando, Florida, Dec. 2002.",
   month = "December",
   year = "2002",
}

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