Abstract
Content analysis of pollen grains in the atmosphere is an important task for preventing allergy symptoms, studying crop production or detecting environmental changes. In the last decades, a lot of palynological labs have been created to collect, prepare and analyse airborne samples. Nowadays, this task is done manually with optical microscopes, requires trained experts and is time-consuming. The development of new computer vision systems and the low price of storage systems have improved the solutions towards an automated palynology. Some recognition problems have been solved with better quality images and other with 3D images, but localisation in real airborne samples, with debris, clumped and grouped pollen grains needs to be improved in order to achieve an automatic system useful for biological labs. In this manuscript, we analyse the advances achieved in the last years and explain a new low-cost methodology, that imitates the human expert labour using computational algorithms based on image characteristics and domain knowledge to detect pollen grains. The current results are promising (81.92% of recall and 18.5% of precision) but not enough to develop an automated palynology system.
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Díaz-López, E. et al. (2015). Localisation of Pollen Grains in Digitised Real Daily Airborne Samples. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_37
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DOI: https://doi.org/10.1007/978-3-319-18914-7_37
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