Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition
Date
2006
Authors
Draper, Bruce A., author
Roberts, Rodney G., author
Maciejewski, Anthony A., author
Saitwal, Kishor, author
IEEE, publisher
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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 a priori how this will affect the quality of the resulting eigendecomposition. The work presented here provides an analysis of how different resolution reduction techniques affect the eigendecomposition. A computationally efficient algorithm for calculating the eigendecomposition based on this analysis is proposed. Examples show that this algorithm performs well on arbitrary video sequences.
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Rights Access
Subject
computational complexity
computer vision
correlation
data compression
eigenspace
image resolution
image sampling
image sequences
singular value decomposition (SVD)
video coding