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Volume: 12 | Article ID: art00012
Spectral Imaging Target Development Based on Hierarchical Cluster Analysis
  DOI :  10.2352/CIC.2004.12.1.art00012  Published OnlineJanuary 2004

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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Mahnaz Mohammadi, Mahdi Nezamabadi, Roy S. Berns, Lawrence A. Taplin, "Spectral Imaging Target Development Based on Hierarchical Cluster Analysisin Proc. IS&T 12th Color and Imaging Conf.,  2004,  pp 59 - 64,

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