---
_id: '1533'
abstract:
- lang: eng
text: 'In a large number of scientific areas, such as immunology, forensics, paleoecology,
and archeology, the study of pollen, i.e., palynology, plays an important role:
from tracking climate changes, studying allergies, to forensic investigations
or honey origin analysis. Since the mid-nineties of the last century, the idea
for an automated solution to the problem of pollen identification and classification
was formulated and since then, several attempts and proposals have been made and
presented, based on different technologies, in particular in the field of Computer
Vision. However, as of 2021 microscopic analyses are performed mainly manually
by highly trained specialists, although the capabilities of artificial intelligence,
especially Deep Neural Networks, are steadily increasing. In this work, we analyzed
various state-of-the-art research work concerning pollen detection and classification
and compared their methods and results. The problems, such as data accessibility,
different methods of Machine Learning, and the intended applicability of the proposed
solutions are explored. We also identified crucial issues that require further
work and research. Our work will provide a thorough view on the current state
of the art, its issues, and possibilities for the future.'
author:
- first_name: Philipp
full_name: Viertel, Philipp
id: '216274'
last_name: Viertel
orcid: 0000-0002-7274-4290
- first_name: Matthias
full_name: König, Matthias
id: '213498'
last_name: König
orcid: 0000-0002-4915-0750
citation:
alphadin: 'Viertel, Philipp ; König, Matthias: Pattern Recognition Methodologies
for Pollen Grain Image Classification: A Survey. In: Machine Vision and Applications
Bd. 33, Springer (2022)'
ama: 'Viertel P, König M. Pattern Recognition Methodologies for Pollen Grain Image
Classification: A Survey. Machine Vision and Applications. 2022;33. doi:10.1007/s00138-021-01271-w'
apa: 'Viertel, P., & König, M. (2022). Pattern Recognition Methodologies for
Pollen Grain Image Classification: A Survey. Machine Vision and Applications,
33. https://doi.org/10.1007/s00138-021-01271-w'
bibtex: '@article{Viertel_König_2022, title={Pattern Recognition Methodologies for
Pollen Grain Image Classification: A Survey}, volume={33}, DOI={10.1007/s00138-021-01271-w},
journal={Machine Vision and Applications}, publisher={Springer}, author={Viertel,
Philipp and König, Matthias}, year={2022} }'
chicago: 'Viertel, Philipp, and Matthias König. “Pattern Recognition Methodologies
for Pollen Grain Image Classification: A Survey.” Machine Vision and Applications
33 (2022). https://doi.org/10.1007/s00138-021-01271-w.'
ieee: 'P. Viertel and M. König, “Pattern Recognition Methodologies for Pollen Grain
Image Classification: A Survey,” Machine Vision and Applications, vol.
33, 2022.'
mla: 'Viertel, Philipp, and Matthias König. “Pattern Recognition Methodologies for
Pollen Grain Image Classification: A Survey.” Machine Vision and Applications,
vol. 33, Springer, 2022, doi:10.1007/s00138-021-01271-w.'
short: P. Viertel, M. König, Machine Vision and Applications 33 (2022).
date_created: 2021-11-10T12:18:04Z
date_updated: 2023-06-15T11:33:51Z
department:
- _id: '102'
doi: 10.1007/s00138-021-01271-w
intvolume: ' 33'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1007/s00138-021-01271-w
oa: '1'
publication: Machine Vision and Applications
publication_status: published
publisher: Springer
status: public
title: 'Pattern Recognition Methodologies for Pollen Grain Image Classification: A
Survey'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: '245590'
volume: 33
year: '2022'
...