, Image processing with ImageJ. Biophotonics International, vol.11, issue.7, pp.36-42, 2004.
Counting in the wild, European Conference on Computer Vision, pp.483-498, 2016. ,
Crowdnet: A deep convolutional network for dense crowd counting, Proceedings of the 24th ACM International Conference on Multimedia, pp.640-644, 2016. ,
Maximizing human effort for analyzing scientific images: A case study using digitized herbarium sheets, Applications in Plant Sciences, vol.8, issue.6, p.11370, 2020. ,
Going deeper in the automated identification of herbarium specimens, BMC Evolutionary Biology, vol.17, issue.1, p.181, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01580070
Instance segmentation for fine detection of crop and weed by precision agricultural robots, Applications in Plant Sciences, vol.8, issue.7, p.11373, 2020. ,
Counting everyday objects in everyday scenes, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4428-4437, 2017. ,
Herbarium records are reliable sources of phenological change driven by climate and provide novel insights into species' phenological cueing mechanisms, American Journal of Botany, vol.102, issue.10, pp.1599-1609, 2015. ,
Plant identification based on noisy web data: The amazing performance of deep learning, Working Notes of the Conference and Labs of the Evaluation Forum, pp.11-14, 2017. ,
,
Fine-grained automated visual analysis of herbarium specimens for phenological data extraction: An annotated dataset of reproductive organs in Strepanthus herbarium specimens. Available at Zenodo repository, 2020. ,
Mask R-CNN, Proceedings of the IEEE International Conference on Computer Vision, pp.2961-2969, 2017. ,
Using herbaria to study global environmental change, New Phytologist, vol.221, issue.1, pp.110-122, 2019. ,
Microsoft COCO: Common objects in context, European Conference on Computer Vision, pp.740-755, 2014. ,
Feature pyramid networks for object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2117-2125, 2017. ,
Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras, Applications in Plant Sciences, vol.7, issue.3, p.1233, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02137748
A new phenological metric for use in pheno-climatic models: A case study using herbarium specimens of Streptanthus tortuosus, Applications in Plant Sciences, vol.7, issue.7, p.11276, 2019. ,
Scored phenology and climate data from a set of Streptanthus tortuosus herbarium specimens, 2019. ,
Applying machine learning to investigate long term insect-plant interactions preserved on digitized herbarium specimens, Applications in Plant Sciences, vol.8, issue.6, p.11369, 2020. ,
Maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch, 2018. ,
Increased variance in temperature and lag effects alter phenological responses to rapid warming in a subarctic plant community, Global Change Biology, vol.23, issue.2, pp.801-814, 2017. ,
Climate drives shifts in grass reproductive phenology across the western USA, New Phytologist, vol.213, issue.4, pp.1945-1955, 2017. ,
A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, pp.62-66, 1979. ,
GinJinn: An object-detection pipeline for feature extraction from herbarium specimens, Applications in Plant Sciences, vol.8, issue.6, p.11351, 2020. ,
Herbarium specimens reveal substantial and unexpected variation in phenological sensitivity across the eastern United States, Philosophical Transactions of the Royal Society B, vol.374, 1763. ,
Overlooked climate parameters best predict flowering onset: Assessing phenological models using the elastic net, Global Change Biology, vol.24, issue.12, pp.5972-5984, 2018. ,
Automatic differentiation in PyTorch, NIPS 2017 Autodiff Workshop: The future of gradient-based machine learning software and techniques, 2017. ,
Spring-and fall-flowering species show diverging phenological responses to climate in the Southeast USA, International Journal of Biometeorology, vol.63, issue.4, pp.481-492, 2019. ,
A new method and insights for estimating phenological events from herbarium specimens, Applications in Plant Sciences, vol.7, issue.3, p.1224, 2019. ,
Herbarium specimens demonstrate earlier flowering times in response to warming in Boston, American Journal of Botany, vol.91, issue.8, pp.1260-1264, 2004. ,
Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum), Applications in Plant Sciences, vol.8, issue.6, p.11372, 2020. ,
DeepSetNet: Predicting sets with deep neural networks, 2017 IEEE International Conference on Computer Vision, pp.5257-5266, 2017. ,
Learning to count with deep object features, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.90-96, 2015. ,
Digitization of herbaria enables novel research, American Journal of Botany, vol.104, issue.9, pp.1281-1284, 2017. ,
Green digitization: Online botanical collections data answering real-world questions, Applications in Plant Sciences, vol.6, issue.2, p.1028, 2018. ,
LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens, Applications in Plant Sciences, vol.8, issue.6, p.11367, 2020. ,
Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning, Applications in Plant Sciences, vol.8, issue.6, p.11352, 2020. ,
Old plants, new tricks: Phenological research using herbarium specimens, Trends in Ecology and Evolution, vol.32, issue.7, pp.531-546, 2017. ,
CrowdCurio: An online crowdsourcing platform to facilitate climate change studies using herbarium specimens, New Phytologist, vol.215, pp.479-488, 2017. ,
Digitization protocol for scoring reproductive phenology from herbarium specimens of seed plants, Applications in Plant Sciences, vol.6, issue.2, p.1022, 2018. ,