Automatic Pollen Grains Counter

This paper deals with the problem of counting the amount of pollen grains (density of pollen) in a view acquired on a daily basis by a dedicated device. The grains are stuck on a ribbon which is analyzed by a microscope. This task is currently performed by a human operator who has to analyze quite 300 microscopic slides representing 10% of the ribbon. This task requires a particularly high concentration. Moreover, the viewer has to identify the pollen grains among water drops and soot spots. We propose an algorithm composed of four main steps to perform this task automatically. The basic detection of pollen grains, relying on their shape and color, is completed by pre-processing and post-processing operations to handle specific cases like broken grains or grains at the border of images. Finally the efficiency is improved by using a neural network to refine the results. Our automatic counter has been compared with the pollen density obtained by the manual counting and our program has proved its high accuracy.