Fast eigenspace decomposition of correlated images using their low-resolution properties Saitwal, Kishor ; Maciejewski, Anthony A. ; Roberts, Rodney G. "This work was supported by the National Imagery and Mapping Agency under contract no. 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 very well on arbitrary video sequences. Colorado State University. Libraries 2004 text ; image application/pdf ECEaam00107.pdf FACFECEN100107ARTI eng c2004 IEEE
Fast eigenspace decomposition of correlated images using their low-resolution properties
Saitwal, Kishor ; Maciejewski, Anthony A. ; Roberts, Rodney G.
"This work was supported by the National Imagery and Mapping Agency under contract no. 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 very well on arbitrary video sequences.
Colorado State University. Libraries
2004
text ; image
application/pdf
ECEaam00107.pdf
FACFECEN100107ARTI
eng
c2004 IEEE