Abstract: In the last years the need to numerically define color by its coordinates in n-dimensional space has increased strongly. Colorimetric calibration is fundamental in food processing and other biological disciplines to quantitatively compare samples’ color during workflow with many devices. Several software programmes are available to perform standardized colorimetric procedures, but they are often too imprecise for scientific purposes. In this study, we applied the Thin-Plate Spline interpolation algorithm to calibrate colours in sRGB space (the corresponding Matlab code is reported in the Appendix). This was compared with other two approaches. The first is based on a commercial calibration system (ProfileMaker) and the second on a Partial Least Square analysis. Moreover, to explore device variability and resolution two different cameras were adopted and for each sensor, three consecutive pictures were acquired under four different light conditions. According to our results, the Thin-Plate Spline approach reported a very high efficiency of calibration allowing the possibility to create a revolution in the in-field applicative context of colour quantification not only in food sciences, but also in other biological disciplines. These results are of great importance for scientific color evaluation when lighting conditions are not controlled. Moreover, it allows the use of low cost instruments while still returning scientifically sound quantitative data.
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Menesatti, P.; Angelini, C.; Pallottino, F.; Antonucci, F.; Aguzzi, J.; Costa, C. RGB Color Calibration for Quantitative Image Analysis: The “3D Thin-Plate Spline” Warping Approach. Sensors 2012, 12, 7063-7079.
Menesatti P, Angelini C, Pallottino F, Antonucci F, Aguzzi J, Costa C. RGB Color Calibration for Quantitative Image Analysis: The “3D Thin-Plate Spline” Warping Approach. Sensors. 2012; 12(6):7063-7079.
Menesatti, Paolo; Angelini, Claudio; Pallottino, Federico; Antonucci, Francesca; Aguzzi, Jacopo; Costa, Corrado. 2012. "RGB Color Calibration for Quantitative Image Analysis: The “3D Thin-Plate Spline” Warping Approach." Sensors 12, no. 6: 7063-7079.