Part 9: Feature ExtractionInternational audienceThe correct classification of airborne pollen is relevant for medical treatment of allergies, and the regular manual process is costly and time consuming. Aiming at automatic processing, we propose a set of relevant image-based features for the recognition of top allergenic pollen taxa. The foundation of our proposal is the testing and evaluation of features that can properly describe pollen in terms of shape, texture, size and apertures. In this regard, a new flexible aperture detector is incorporated to the tests. The selected set is demonstrated to overcome the intra-class variance and inter-class similarity in a SVM classification scheme with a performance comparable to the state of the art procedures

Abstract. The taxonomical recognition of microscopic biological particles such as pollen and spores is relevant for medical and aerobiological applications. Focusing on an accurate and automatic vision-based pollen recognition system, we propose a method for classification of pollen apertures based on bag-of-words strategy, with the ability of learning new types from different taxa without the need of new algorithms. Results demonstrate suitable performance and ability to add new taxa.

Accurate recognition of airborne pollen taxa is crucial for understanding and treating allergic diseases which affect an important proportion of the world population. Modern computer vision techniques enable the detection of discriminant characteristics. Apertures are among the important characteristics which have not been adequately explored until now. A flexible method of detection, localization, and counting of apertures of different pollen taxa with varying appearances is proposed. Aperture description is based on primitive images following the bag-of-words strategy. A confidence map is estimated based on the classification of sampled regions. The method is designed to be extended modularly to new aperture types employing the same algorithm by building individual classifiers. The method was evaluated on the top five allergenic pollen taxa in Germany, and its robustness to unseen particles was verified.