Mario Valerio Giuffrida
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network
Giuffrida, Mario Valerio; Scharr, Hanno; Tsaftaris, Sotirios A
Authors
Hanno Scharr
Sotirios A Tsaftaris
Abstract
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems , such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversar-ial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DC-GAN (a recent adversarial network model using convolu-tional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128×128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) |
Start Date | Oct 22, 2017 |
End Date | Oct 29, 2017 |
Acceptance Date | Aug 1, 2017 |
Online Publication Date | Jan 23, 2018 |
Publication Date | Sep 1, 2017 |
Deposit Date | Sep 24, 2019 |
Publisher | Institute of Electrical and Electronics Engineers |
Series ISSN | 2473-9944 |
DOI | https://doi.org/10.1109/ICCVW.2017.242 |
Public URL | http://researchrepository.napier.ac.uk/Output/2157954 |
Publisher URL | http://openaccess.thecvf.com/menu.py |
You might also like
Citizen crowds and experts: observer variability in image-based plant phenotyping
(2018)
Journal Article
Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting
(2018)
Journal Article
Learning to Count Leaves in Rosette Plants
(2015)
Presentation / Conference Contribution
Multimodal MR Synthesis via Modality-Invariant Latent Representation
(2017)
Journal Article
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search