Towards automation of palynology 2: the use of texture measures and neural network analysis for automated identification of optical images of pollen grains

Abstract The automation of palynology (the identification and counting of pollen grains and spores) will be a small step for image recognition, but a giant stride for palynology. Here we show the first successful automated identification, with 100% accuracy, of a realistic number of taxa. The technique used involves a neural network classifier applied to surface texture data from light microscope images. A further significance of the technique is that it could be adapted for the identification of a wide range of biological objects, both microscopic and macroscopic. Copyright © 2004 John Wiley & Sons, Ltd.