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2016 | OriginalPaper | Buchkapitel

Pollen Recognition for Allergy and Asthma Management Using GIST Features

verfasst von : Natalia Khanzhina, Evgeny Putin

Erschienen in: Digital Transformation and Global Society

Verlag: Springer International Publishing

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Abstract

In this paper we propose a way of managing allergy and asthma based on pollen recognition using images from an optical microscope. GIST descriptors are extracted as features. Our research can help to automate a time-consuming process of pollen grains classification, which is usually performed by highly qualified palynologists, and to create a real-time system of immediate notification about high atmospheric allergenic pollen concentration. Standard machine learning methods are applied and results are compared on different pollen datasets. The best model is support vector machine with 95.2% of accuracy on 9 pollen species and 98.3% on 5 pollen species.

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Titel: Pollen Recognition for Allergy and Asthma Management Using GIST Features
verfasst von: Natalia Khanzhina
Evgeny Putin
Verlag: Springer International Publishing
Buch: Digital Transformation and Global Society
Print ISBN: 978-3-319-49699-3

Electronic ISBN: 978-3-319-49700-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-49700-6_51

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