Using the Low-Resolution Properties of Correlated Images to Improve the Computational Efficiency of Eigenspace Decomposition

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Using the Low-Resolution Properties of Correlated Images to Improve the Computational Efficiency of Eigenspace Decomposition (2006)

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by Kishor Saitwal , Anthony A. Maciejewski , Rodney G. Roberts , Bruce A. Draper

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@MISC{Saitwal06usingthe,
    author = {Kishor Saitwal and Anthony A. Maciejewski and Rodney G. Roberts and Bruce A. Draper},
    title = {Using the Low-Resolution Properties of Correlated Images to Improve the Computational Efficiency of Eigenspace Decomposition},
    year = {2006}
}

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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.

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