Monitoring color in the production line requires to remotely observe moving and not-aligned objects with in general complex surface features: multicolored, textured, non-flat, showing highlights and shadows. We discuss the use of color cameras and associated color image processing technologies for what we call 'imaging colorimetry.' This is a 2-step procedure which first uses color for segmentation and for finding Regions-of- Interest on the moving objects and then uses cluster-based color image processing for computing color deviations relative to previously trained references. This colorimetry is much more a measurement of aesthetic consistency of the visual appearance of a product then the traditional measurement of a more physically defined mean color vector difference. We show how traditional non-imaging colorimetry looses most of this aesthetic information due to the computation of a mean color vector or mean color vector difference, by averaging over the sensor's field-of-view. A large number of industrial applications are presented where complex inspection tasks have been solved based on this approach. The expansion to a higher feature space dimensions based on the 'multisensorial camera' concept gives an outlook to future developments.
Robert Charles Massen,
"Online color monitoring", Proc. SPIE 3826, Polarization and Color Techniques in Industrial Inspection, (16 September 1999); doi: 10.1117/12.364309; https://doi.org/10.1117/12.364309