Classification of dates varieties and effect of motion blurring on standardized moment features

Computer vision technology has been used as a successful non-destructive quality assessment tool for various food products. In general, several features are extracted from the images of interest, and used for the classification models. Furthermore, in most of the studies, static images have been used in the calibration and evaluation models. Classification models with a reduced number of features, and a mechanism to test the capability of the algorithm for moving objects by means of simulating the blurring effect on the static images would be beneficial to determine the performance of the system in real-time quality monitoring in industries. Using three date varieties as model food, motion was simulated for the date's images and a successful neural network classifier was designed with only three statistical features (mean, standard deviation, and skewness). The reduced number of features and simplicity of the classifier yielded a solution that can be potentially implemented in hardware fast enough so that to consider the case of classification of the dates in a conveyor belt. To test the solution under such conditions, a blurring degradation function was used to verify that the classifier would work. The effects that motion blurring causes to these statistical moments in a general sense were examined using random numbers drawn from the distribution in the Pearson system. Because motion blurring showed a tendency to change the distribution to a Gaussian density, the same features and classifier yielded similar results despite of motion.