Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition - Draper, Bruce A., author; Roberts, Rodney G., author; Maciejewski, Anthony A., author; Saitwal, Kishor, author; IEEE, publisher (Colorado State University. Libraries, 2006)
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.
Keywords: computational complexity; computer vision; correlation; data compression; eigenspace; image resolution; image sampling; image sequences; singular value decomposition (SVD); video coding