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Title:

Automatic Pollen Classification and Segmentation Using U-Nets and Synthetic Data

Document type:
Article
Author(s):
Boldeanu, Mihai; Gonzalez-Alonso, Monica; Cucu, Horia; Burileanu, Corneliu; Maya-Manzano, Jose Maria; Buters, Jeroen Titus Maria
Abstract:
Pollen allergies have become one of the most wide-spread afflictions that impact quality of life. This has made automatic pollen detection, classification and monitoring a very important topic of research. This paper introduces a new public annotated image data-set of pollen with almost 45 thousand samples obtained from an automatic instrument. In this work we apply some of the best performing convolutional neural networks architectures on the task of pollen classification as well as some fully...     »
Journal title abbreviation:
IEEE Access
Year:
2022
Journal volume:
10
Pages contribution:
73675-73684
Fulltext / DOI:
doi:10.1109/ACCESS.2022.3189012
Print-ISSN:
2169-3536
TUM Institution:
Lehrstuhl für Molekulare Allergologie und Umweltforschung (Prof. Schmidt-Weber)
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