--- _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' ...