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A comparison of eigendecomposition for sets of correlated images at different resolutions

Date

2003

Authors

Roberts, Rodney G., author
Maciejewski, Anthony A., author
Saitwal, Kishor, author
IEEE, publisher

Journal Title

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Abstract

Eigendecomposition is a common technique that is performed on sets of correlated images in a number of computer vision and robotics applications. Unfortunately, the computation of an eigendecomposition can become prohibitively expensive when dealing with very high resolution images. While reducing the resolution of the images will reduce the computational expense, it is not known how this will affect the quality of the resulting eigendecomposition. The work presented here proposes a framework for quantifying the effects of varying the resolution of images on the eigendecomposition that is computed from those images. Preliminary results show that an eigendecomposition from low-resolution images may be nearly as effective in some applications as those from high-resolution images.

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Subject

image resolution
eigenvalues and eigenfunctions
computer vision
matrix algebra
singular value decomposition

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