Agglomerative hierarchical cluster analysis was used to group similar spectra from a large database of samples. Based on angles between reflectance vectors of members of a cluster, a reflectance vector was selected as representative of that cluster. Representative samples were grouped together and stored as new calibration targets. Simulated wide-band imaging with glass filters was performed using these new calibration targets and a transformation matrix from digital signals to reflectance was derived. Different verification targets were reconstructed using the transformation matrix; the spectral and colorimetric accuracy of the reconstruction was evaluated. It was shown that beyond a threshold number of samples in the calibration target, the performance of reconstruction became independent of the number of samples used in the calculation. The average spectral RMS for a calibration target consisting of 24 samples selected based on clustering were found to be less than 3.2% for GretagMacbeth ColorChecker DC, GretagMacbeth ColorChecker Rendition Chart, and Esser Test Chart TE221.

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This article may be accessed on the publisher's website (additional fees may apply) at: http://www.imaging.org/store/epub.cfm?abstrid=32121 This research was supported by the Andrew W. Mellon Foundation, the National Gallery of Art, Washington, DC, the Museum of Modern Art, New York, and Rochester Institute of Technology. ISBN:0-89208-254-2Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type


Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


RIT – Main Campus