Automated and accurate segmentation of leaf venation networks via deep learning-Reference-Cited by-同舟云学术

Automated and accurate segmentation of leaf venation networks via deep learning

Published:2020-10-10 Issue:1 Volume:229 Page:631-648
ISSN:0028-646X
Container-title:New Phytologist
language:en
Short-container-title:New Phytol

Author:

Xu Hao¹,Blonder Benjamin²³^ORCID,Jodra Miguel²,Malhi Yadvinder²^ORCID,Fricker Mark⁴^ORCID

Affiliation:

1. Department of Engineering Science Institute of Biomedical Engineering University of Oxford Old Road Campus Research Building OxfordOX3 7DQUK

2. Environmental Change Institute School of Geography and the Environment University of Oxford South Parks Road OxfordOX1 3QYUK

3. Department of Environmental Science, Policy, and Management University of California 120 Mulford Hall Berkeley CA94720USA

4. Department of Plant Sciences University of Oxford South Parks Road OxfordOX1 3RBUK

Funder

Human Frontier Science Program

Natural Environment Research Council

National Science Foundation

Leverhulme Trust

H2020 European Research Council

Publisher

Wiley

Subject

Plant Science,Physiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/nph.16923

Reference91 articles.

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3. BernsenJ.1986.Dynamic thresholding of gray‐level images.Proceedings of the Eighth International Conference on Pattern Recognition Paris France 1251–1255.

4. Leaf venation networks of Bornean trees: images and hand‐traced segmentations

5. Linking functional traits to multiscale statistics of leaf venation networks

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