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Leveraging Multiple Datasets for Deep Leaf Counting
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While stateof-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation). Instead, our method treats
doi:10.1109/iccvw.2017.243
dblp:conf/iccvw/DobrescuGT17
fatcat:b6j4r6itvvfzrnqoqyogjb2rv4