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The aim of the proposed challenge is the automatic classification of pollen grain images exploiting the largest dataset of microscope pollen grain images, collected from aerobiological samples. The microscope images of the samples have been digitalized and processed through a proper image processing pipeline to detect and extract four classes of objects, including three species of pollen grain and an additional class of objects that could be often mis-classified as pollen (e.g., air bubbles, dust, etc.). More than 13.000 objects have been detected and labelled by aerobiology experts.
The provided dataset consists of more than 13 thousands per-object images collected from aerobiological samples, classified into four different categories:
Figure 1 – Example of class 1 (Normal Pollen)
Figure 2 – Example of class 2 (Anomalous Pollen)
Figure 3 – Example of class 3 (Alnus)
Figure 4 – Example of class 4 (Debris)