Computer vision is becoming increasingly important in quality control of many food processes. The appearance properties of food products (colour, texture, shape and size) are, in fact, correlated with organoleptic characteristics and/or the presence of defects. Quality control based on image processing eliminates the subjectivity of human visual inspection, allowing rapid and non-destructive analysis. However, most food matrices show a wide variability in appearance features, therefore robust and customized image elaboration algorithms have to be implemented for each specific product. For this reason, quality control by visual inspection is still rather diffused in several food processes. The case study inspiring this paper concerns the production of frozen mixed berries. Once frozen, different kinds of berries are mixed together, in different amounts, according to a recipe. The correct quantity of each kind of fruit, within a certain tolerance, has to be ensured by producers. Quality control relies on bringing few samples for each production lot (samples of the same weight) and, manually, counting the amount of each species. This operation is tedious, subject to errors, and time consuming, while a computer vision system (CVS) could determine the amount of each kind of berries in a few seconds. This paper discusses the problem of colour calibration of the CVS used for frozen berries mixture evaluation. Images are acquired by a digital camera coupled with a dome lighting system, which gives a homogeneous illumination on the entire visible surface of the berries, and a flat bed scanner. RBG device dependent data are then mapped onto CIELab colorimetric colour space using different transformation operators. The obtained results show that the proposed calibration procedure leads to colour discrepancies comparable or even below the human eyes sensibility.
computer vision, colour calibration, food quality, frozen berries.