Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition

Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition

Saitwal, Kishor ; Maciejewski, Anthony A. ; Roberts, Rodney G. ; Draper, Bruce A. (Bruce Austin), 1962-

"This work was supported by the National Imagery and Mapping Agency under Contract NMA201-00-1-1003 and through collaborative participation in the Robotics Consortium sponsored by the U. S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0012."

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.

Colorado State University. Libraries

2006

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ECEaam00046.pdf

FACFECEN100046ARTI

eng

English

c2006, IEEE