Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition
dc.contributor.author | Draper, Bruce A., author | |
dc.contributor.author | Roberts, Rodney G., author | |
dc.contributor.author | Maciejewski, Anthony A., author | |
dc.contributor.author | Saitwal, Kishor, author | |
dc.contributor.author | IEEE, publisher | |
dc.date.accessioned | 2007-01-03T07:26:31Z | |
dc.date.available | 2007-01-03T07:26:31Z | |
dc.date.issued | 2006 | |
dc.description.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. | |
dc.description.sponsorship | 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. | |
dc.format.medium | born digital | |
dc.format.medium | articles | |
dc.identifier.bibliographicCitation | Saitwal, Kishor, et al., Using the Low-Resolution Properties of Correlated Images to Improve the Computational Efficiency of Eigenspace Decomposition, IEEE Transactions on Image Processing 15, no. 8 (August 2006): 2376-2387. | |
dc.identifier.uri | http://hdl.handle.net/10217/620 | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Colorado State University. Libraries | |
dc.relation.ispartof | Faculty Publications | |
dc.rights | ©2006 IEEE. | |
dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
dc.subject | computational complexity | |
dc.subject | computer vision | |
dc.subject | correlation | |
dc.subject | data compression | |
dc.subject | eigenspace | |
dc.subject | image resolution | |
dc.subject | image sampling | |
dc.subject | image sequences | |
dc.subject | singular value decomposition (SVD) | |
dc.subject | video coding | |
dc.title | Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition | |
dc.type | Text |
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