Draper, Bruce A., authorRoberts, Rodney G., authorMaciejewski, Anthony A., authorSaitwal, Kishor, authorIEEE, publisher2007-01-032007-01-032006Saitwal, 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.http://hdl.handle.net/10217/620Eigendecomposition 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.born digitalarticleseng©2006 IEEE.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.computational complexitycomputer visioncorrelationdata compressioneigenspaceimage resolutionimage samplingimage sequencessingular value decomposition (SVD)video codingText