AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images
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.
Cite
Text
Ubbens et al. "AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images." European Conference on Computer Vision Workshops, 2020. doi:10.1007/978-3-030-65414-6_27Markdown
[Ubbens et al. "AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images." European Conference on Computer Vision Workshops, 2020.](https://mlanthology.org/eccvw/2020/ubbens2020eccvw-autocount/) doi:10.1007/978-3-030-65414-6_27BibTeX
@inproceedings{ubbens2020eccvw-autocount,
title = {{AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images}},
author = {Ubbens, Jordan R. and Ayalew, Tewodros W. and Shirtliffe, Steve and Josuttes, Anique and Pozniak, Curtis and Stavness, Ian},
booktitle = {European Conference on Computer Vision Workshops},
year = {2020},
pages = {391-399},
doi = {10.1007/978-3-030-65414-6_27},
url = {https://mlanthology.org/eccvw/2020/ubbens2020eccvw-autocount/}
}