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
Journal ISSN
Volume Title
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.
Description
Rights Access
Subject
image resolution
eigenvalues and eigenfunctions
computer vision
matrix algebra
singular value decomposition