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Data Augmentation From RGB to Chlorophyll Fluorescence Imaging Application to Leaf Segmentation of Arabidopsis Thaliana From Top View Images

机译:从RGB到叶绿素荧光成像应用的数据增强,从顶视图图像征征拟南芥的分割

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In this report we investigate various strategies to boost the performance for leaf segmentation of Arabidopsis thaliana in chlorophyll fluorescent imaging without any manual annotation. Direct conversion of RGB images to gray levels picked from CVPPP challenge or from a virtual Arabidopsis thaliana simulator are tested together with synthetic noisy versions of these. Segmentation performed with a state of the art U-Net convolutional neural network is shown to benefit from these approaches with a Dice coefficient between 0.95 and 0.97 on the segmentation of the border of the leaves. A new annotated dataset of fluorescent images is made available.
机译:在本报告中,我们调查了各种策略,以提高叶绿素荧光成像拟南芥拟南芥叶片细分的性能,而无需任何手动注释。 RGB图像的直接转换为从CVPPP挑战或虚拟拟南芥挑选的灰度级别挑选到虚拟拟南芥模拟器,与它们的合成嘈杂版本一起测试。用现有技术的U-Net卷积神经网络进行的分割显示,从这些方法中受益于骰子系数之间的骰子系数0.95和0.97的叶子边界的分割。提供了新的注释数据集的荧光图像。

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