Standard
Fungi recognition : A practical use case. / Sulc, Milan; Picek, Lukas; Matas, Jiri; Jeppesen, Thomas S.; Heilmann-Clausen, Jacob.
Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 2305-2313 9093624 (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Sulc, M, Picek, L, Matas, J, Jeppesen, TS
& Heilmann-Clausen, J 2020,
Fungi recognition: A practical use case. in
Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020., 9093624, Institute of Electrical and Electronics Engineers Inc., Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, pp. 2305-2313, 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020, Snowmass Village, United States,
01/03/2020.
https://doi.org/10.1109/WACV45572.2020.9093624APA
Sulc, M., Picek, L., Matas, J., Jeppesen, T. S.
, & Heilmann-Clausen, J. (2020).
Fungi recognition: A practical use case. In
Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 2305-2313). [9093624] Institute of Electrical and Electronics Engineers Inc.. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
https://doi.org/10.1109/WACV45572.2020.9093624Vancouver
Sulc M, Picek L, Matas J, Jeppesen TS
, Heilmann-Clausen J.
Fungi recognition: A practical use case. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 2305-2313. 9093624. (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020).
https://doi.org/10.1109/WACV45572.2020.9093624Author
Sulc, Milan ; Picek, Lukas ; Matas, Jiri ; Jeppesen, Thomas S. ; Heilmann-Clausen, Jacob. / Fungi recognition : A practical use case. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 2305-2313 (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020).
Bibtex
@inproceedings{72c58c2101ee4fb79ff661dc6b1088de,
title = "Fungi recognition: A practical use case",
abstract = "The paper presents a system for visual recognition of 1394 fungi species based on deep convolutional neural networks and its deployment in a citizen-science project. The system allows users to automatically identify observed specimens, while providing valuable data to biologists and computer vision researchers. The underlying classification method scored first in the FGVCx Fungi Classification Kaggle competition organized in connection with the Fine-Grained Visual Categorization (FGVC) workshop at CVPR 2018. We describe our winning submission and evaluate all technicalities that increased the recognition scores, and discuss the issues related to deployment of the system via the web- and mobile- interfaces.",
author = "Milan Sulc and Lukas Picek and Jiri Matas and Jeppesen, {Thomas S.} and Jacob Heilmann-Clausen",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 ; Conference date: 01-03-2020 Through 05-03-2020",
year = "2020",
doi = "10.1109/WACV45572.2020.9093624",
language = "English",
series = "Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020",
pages = "2305--2313",
booktitle = "Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}
RIS
TY - GEN
T1 - Fungi recognition
T2 - 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020
AU - Sulc, Milan
AU - Picek, Lukas
AU - Matas, Jiri
AU - Jeppesen, Thomas S.
AU - Heilmann-Clausen, Jacob
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020
Y1 - 2020
N2 - The paper presents a system for visual recognition of 1394 fungi species based on deep convolutional neural networks and its deployment in a citizen-science project. The system allows users to automatically identify observed specimens, while providing valuable data to biologists and computer vision researchers. The underlying classification method scored first in the FGVCx Fungi Classification Kaggle competition organized in connection with the Fine-Grained Visual Categorization (FGVC) workshop at CVPR 2018. We describe our winning submission and evaluate all technicalities that increased the recognition scores, and discuss the issues related to deployment of the system via the web- and mobile- interfaces.
AB - The paper presents a system for visual recognition of 1394 fungi species based on deep convolutional neural networks and its deployment in a citizen-science project. The system allows users to automatically identify observed specimens, while providing valuable data to biologists and computer vision researchers. The underlying classification method scored first in the FGVCx Fungi Classification Kaggle competition organized in connection with the Fine-Grained Visual Categorization (FGVC) workshop at CVPR 2018. We describe our winning submission and evaluate all technicalities that increased the recognition scores, and discuss the issues related to deployment of the system via the web- and mobile- interfaces.
U2 - 10.1109/WACV45572.2020.9093624
DO - 10.1109/WACV45572.2020.9093624
M3 - Article in proceedings
AN - SCOPUS:85085504154
T3 - Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
SP - 2305
EP - 2313
BT - Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 March 2020 through 5 March 2020
ER -
