High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts Corneliu T.C. Arsene*1, Stephen Church2, Mark Dickinson2 1School of Arts, Languages and Cultures, University of Manchester, United Kingdom 2Photon Science Institute, University of Manchester, United Kingdom email:
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[email protected] 2018 Abstract Multispectral imaging is an important technique for improving the readability of written or printed text where the letters have faded, either due to deliberate erasing or the ravages of time. Often the text can be read by illumination under a single wavelength of light, but in some cases the multispectral images need enhancement to improve the text clarity. There are many possible enhancement techniques: this paper compares an extended set of dimensionality reduction methods for image processing. We assess 15 dimensionality reduction methods applied to two different manuscripts. This assessment was performed subjectively, by asking the opinions of scholars who were experts in the languages used in the manuscripts, and also by using the Davies-Bouldin and Dunn indexes for evaluating the quality of the resultant image clusters. We found that the Canonical Variates Analysis (CVA) method, implemented in Matlab was superior to all the other tested methods. However, the other approaches may be more suitable in specific circumstances, so we would still recommend that a variety are tried. For example, CVA is a supervised 5 [cs.CV] 18 Jul 5 [cs.CV] clustering technique and therefore it requires considerably more user time and effort than a non-supervised technique such as the Principle Component Analysis approach (PCA).