Learning spectral calibration parameters for color inspection

Light sensor spectral calibration is an ill-defined problem. For the identification problem one needs a priori knowledge of the characteristics of the sensor which is difficult to get in most situations. A new methodology is presented in this paper that does not rely on any a priori knowledge of the sensor's characteristics. The method uses an extended generalized cross-validation function to measure predictability of the identified sensor's spectral behavior. The prediction error is minimized with a hybrid genetic algorithm. Further an extended image formation model is introduced to model changes in additive and multiplicative errors. The calibration problem is formulated to be independent of these changes by previously identifying and removing them from the images.