Modular method of detection, localization, and counting of multiple-taxon pollen apertures using bag-of-words

Abstract

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.

Discrimination of Common Ragweed (Ambrosia artemisiifolia)and Mugwort (Artemisia vulgaris)Based on Bag of Visual Words Model

“…The bag of visual words (BoVW) is an object-based image classification method that has gained interest recently. It was originally developed for document categorization (bag of words) but has already showed its effectiveness for identifying objects from digital images in genetics (Ji et al 2009), medicine (Bromuri et al 2014; Mukti et al 2015; Wang et al 2013), remote sensing (Bahmanyar et al 2015; Sun et al 2012; Vetrivel et al 2016; Yuan and Hu 2015), cell biology (Larsen et al 2014), palynology (Lozano - Vega et al 2014), and face recognition (Faraki et al 2015; Li et al 2011). Up till now, BoVW has not been applied to the classification of plant types.…”