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• W3043292641 abstract "Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the literature using state-of-the-art supervised deep learning techniques. However, the annotation of organs in field images is time-consuming and prone to errors. In this paper, we propose a fully unsupervised technique for counting dense objects such as plant organs. We use a convolutional network-based unsupervised segmentation method followed by two post-hoc optimization steps. The proposed technique is shown to provide competitive counting performance on a range of organ counting tasks in sorghum (S. bicolor) and wheat (T. aestivum) with no dataset-dependent tuning or modifications." @default.
• W3043292641 created "2020-07-23" @default.
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• W3043292641 date "2020-07-17" @default.
• W3043292641 modified "2023-09-27" @default.
• W3043292641 title "AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images" @default.
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• W3043292641 hasPublicationYear "2020" @default.
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