title: Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition.
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.
authors.affiliation: Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA. kishor.saitwal@colostate.edu
authors.forename: Kishor
authors.initials: K
authors.lastname: Saitwal
authors.forename: Anthony A
authors.initials: AA
authors.lastname: Maciejewski
authors.forename: Rodney G
authors.initials: RG
authors.lastname: Roberts
authors.forename: Bruce A
authors.initials: BA
authors.lastname: Draper
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods*