ABSTRACT FORM	
	1)Title 2)Authors 3)Text body

A Semi-Automatic System for Pollen Recognition	
A.	Boucher1, R. Tomczak2, P. Hidalgo3, M. Thonnat1, J. Belmonte4, 
C. Galan3 and P. Bonton2
1 - INRIA, Sophia-Antipolis, France
2 - LASMEA, Blaise Pascal University, Clermont-Ferrand, France
3 - University of Córdoba, Spain
4 - Autonomous University of Barcelona, Cerdanyola del Vallès, Spain	
A semi-automatic system for pollen recognition is studied for the european project ASTHMA.  The goal of such a system is to provide accurate pollen concentration measurements.  This information can be used as well by the palynologists, by the clinicians or by a forecast system to predict pollen dispersion. The hardware is based on a light microscope with 60x objective, an automatic 3D moving table and a RGB camera connected to a PC. The software is composed of two modules: pollen grain extraction and pollen grain recognition.  In the first module, the pollen grains are extracted automatically from a pollen slide coloured with fuchsine.  The system finds the pollen grains, based on the colour information, and focuses on them, with a fast and robust algorithm.  Then, the pollen grains are digitized in 3D.  For each pollen grain, a sequence of 100 colour images is taken, showing the grain at different focus (with a step of 0.5 microns).  In the second module, the pollen grain is analyzed for recognition.  To accomplish the recognition, it is necessary to work on 3D images and to use deep palynological knowledge.  This knowledge describes the pollen types according to their main visible characteristics and to those which are important for recognition.  The strategy used for recognition first detects the pollen grain boundaries in 3D.  Then, the main characteristics are searched for according to their location, using image processing methods like thresholding, gradient analysis, region segmentation and colour analysis.  At present, the pollen grain extraction module has been used and tested to build a database of 350 reference pollen grains (35000 images) with 10 to 20 sequences per pollen type.  Numerous pollen grains from aerobilogical slides have also been digitized.  This pollen image database is used to develop the pollen grain recognition module.  Some pollen structures are identified like the pore with annulus in Poaceae, the reticulum in Olea and similar pollen types and the cytoplasm in Cupressaceae. The preliminary results show the recognition of some pollen types, like Urticaceae or Poaceae or some groups of pollen types, like reticulate group.