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12 April 2010 A subspace learning approach to evaluating the performance of image fusion algorithms
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The fusion of multi-spectral images is an important pre-processing operation for scientists and engineers seeking to design robust detection, recognition and identification (DRI) systems. Due to the multitude of pixellevel fusion algorithms available, there is an extreme need for reliable metrics to analyze their performance. Most recently, subspace learning methods have been applied to the field of information fusion for object recognition and classification. This paper aims to extend the capabilities of existing nonlinear dimensionality reduction algorithms to a new area, evaluating the performance of image fusion algorithms. We prove that distances between points in the low dimensional embedding are essentially equivalent to the results given by estimating the amount of information transfered from source images to resultant fused images (normalized mutual information).
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Kenneth A. Byrd, Harold Szu, and Mohamed Chouikha "A subspace learning approach to evaluating the performance of image fusion algorithms", Proc. SPIE 7703, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VIII, 770310 (12 April 2010);

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