A Non-Contact Computer Vision Based Analysis of Color in Foods

Since commercial colorimeters measure small area with a fixed geometry, the result of color measurement is usually unrepresentative for heterogeneous materials as in many food items. This paper describes a computer vision based approach for the measurement of color in a user defined polygonal area on the digital image of a food product. The algorithm used for color measurement converts the RGB values of the image captured by a digital camera to monitor L*a*b* values using the standard equations. The RGB responses for a captured image vary from one case to another, so, the direct transformation from RGB to L*a*b is not useful to obtain meaningful information about the color. Here, an artificial neural network (ANN) model was used to convert the monitor L*a*b* values into spectrophotometric L*a*b* values. The ANN model was calibrated by using the IT8 color chart consisting of 288 different colored squares which reflect all possible variations in the color space. The Delta E values for the estimated values and the real spectrophotometric values were less than 0.45.