AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images

• URL: http://arxiv.org/abs/2007.09178v1
• Date: Fri, 17 Jul 2020 18:27:47 GMT
• Title: AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images
• Authors: Jordan Ubbens, Tewodros Ayalew, Steve Shirtliffe, Anique Josuttes, Curtis Pozniak, Ian Stavness
• Abstract summary: 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.
• Score: 11.976801748296403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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