MODELLING A FOOD MICROSTRUCTURE BY RANDOM SETS

Abstract

Starting from scanning electron microscope images of some food products, we generate binary images of composite materials. After measuring the covariance and the probability for segments and for squares to be included in the dominant component, we develop a modelling of the microstructure from random sets obtained by thresholding Gaussian random functions. The covariance function of the underlying Gaussian random function is estimated from the experimental covariance of the food products. The validity of the model is checked by comparison of the probability curves for segments and for squares, measured on simulated and on initial images. The approach enables us to generate 3D realisations of the microstructure.