Eigendecomposition of images correlated on S1, S2, and SO(3) using spectral theory
IEEE Transactions on Image Processing
An illustration of eigenspace decomposition for illumination invariant pose estimation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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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.