Title: Fungi Recognition: A Practical Use Case
Authors: Šulc, Milan
Picek, Lukáš
Matas, Jiří
Jeppesen, Thomas
Heilmann-Clausen, Jacob
Citation: ŠULC, M. PICEK, L. MATAS, J. JEPPESEN, T. HEILMANN-CLAUSEN, J.Fungi Recognition: A Practical Use Case. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). Red Hook, NY: IEEE, 2020. s. 2305-2313. ISBN 978-1-72816-553-0, ISSN 2472-6737.
Issue Date: 2020
Publisher: IEEE
Document type: konferenční příspěvek
URI: 2-s2.0-85085504154
ISBN: 978-1-72816-553-0
ISSN: 2472-6737
Keywords in different language: visual recognition, fungi, web- and mobile interfaces
Abstract in different language: The paper presents a system for visual recognition of1394 fungi species based on deep convolutional neuralnetworks and its deployment in a citizen-science project.The system allows users to automatically identify observedspecimens, while providing valuable data to biologists andcomputer vision researchers. The underlying classifica-tion method scored first in the FGVCx Fungi ClassificationKaggle competition organized in connection with the Fine-Grained Visual Categorization (FGVC) workshop at CVPR2018. We describe our winning submission and evaluate alltechnicalities that increased the recognition scores, and dis-cuss the issues related to deployment of the system via theweb- and mobile- interfaces.
Rights: © IEEE
Appears in Collections:Postprinty / Postprints (KKY)
Postprinty / Postprints (NTIS)

Files in This Item:
File SizeFormat 
Sulc_Fungi_Recognition_A_Practical_Use_Case_WACV_2020_paper.pdf1,75 MBAdobe PDFView/Open

Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/42937

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

  1. DSpace at University of West Bohemia
  2. Publikační činnost / Publications
  3. OBD