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- W4298037232 abstract "In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To accomplish this task, we use state-of-the-art deep learning architectures: a deconvolutional network for initial segmentation and a convolutional network for leaf counting. Evaluation is performed on the leaf counting challenge dataset at CVPPP-2017. Despite the small number of training samples in this dataset, as compared to typical deep learning image sets, we obtain satisfactory performance on segmenting leaves from the background as a whole and counting the number of leaves using simple data augmentation strategies. Comparative analysis is provided against methods evaluated on the previous competition datasets. Our framework achieves mean and standard deviation of absolute count difference of 1.62 and 2.30 averaged over all five test datasets." @default.
- W4298037232 created "2022-10-01" @default.
- W4298037232 creator A5003790536 @default.
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- W4298037232 date "2017-08-24" @default.
- W4298037232 modified "2023-09-29" @default.
- W4298037232 title "Leaf Counting with Deep Convolutional and Deconvolutional Networks" @default.
- W4298037232 doi "https://doi.org/10.48550/arxiv.1708.07570" @default.
- W4298037232 hasPublicationYear "2017" @default.
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