A comparative study of approaches to automatic pollen identification

The analysis of pollen grains taken from core samples is an extremely valuable technique for climate reconstruction. There is a great need for an automated classification system which can provide a swift and accurate analysis of the relative amounts of pollen on a microscope slide. Pollen grains have a complex three-dimensional structure and can appear on the microscope slide in any orientation. Despite efforts to improve the preparation of the slides a large amount of debris is also present. This paper describes work being conducted to reliably separate pollen grains from debris. Two methods of pollen identification are compared, a model based approach and a self-training, deformable template neural network. The model based approach is shown to accurately describe the pollen grains used in the test but requires considerable refinement to be useful whereas the neural network provides excellent results comparable to a human operator.