| Plant disease detection using hyperspectral imaging P Moghadam, D Ward, E Goan, S Jayawardena, P Sikka, E Hernandez 2017 International Conference on Digital Image Computing: Techniques and …, 2017 | 149 | 2017 |
| Deep leaf segmentation using synthetic data D Ward, P Moghadam, N Hudson arXiv preprint arXiv:1807.10931, 2018 | 137 | 2018 |
| Learning arbitrary-goal fabric folding with one hour of real robot experience R Lee, D Ward, V Dasagi, A Cosgun, J Leitner, P Corke Conference on Robot Learning, 2317-2327, 2021 | 60 | 2021 |
| Temporally coherent embeddings for self-supervised video representation learning J Knights, B Harwood, D Ward, A Vanderkop, O Mackenzie-Ross, ... 2020 25th International Conference on Pattern Recognition (ICPR), 8914-8921, 2021 | 43 | 2021 |
| Scalable learning for bridging the species gap in image-based plant phenotyping D Ward, P Moghadam Computer Vision and Image Understanding 197, 103009, 2020 | 29 | 2020 |
| On the use of genome‐wide data to model and date the time of anthropogenic hybridisation: An example from the Scottish wildcat J Howard‐McCombe, D Ward, AC Kitchener, D Lawson, HV Senn, ... Molecular Ecology 30 (15), 3688-3702, 2021 | 27 | 2021 |
| Investigating the impact of model misspecification in neural simulation-based inference P Cannon, D Ward, SM Schmon arXiv preprint arXiv:2209.01845, 2022 | 23 | 2022 |
| Robust neural posterior estimation and statistical model criticism D Ward, P Cannon, M Beaumont, M Fasiolo, S Schmon Advances in Neural Information Processing Systems 35, 33845-33859, 2022 | 20 | 2022 |
| Deep leaf segmentation using synthetic data. arXiv 2018 D Ward, P Moghadam, N Hudson arXiv preprint arXiv:1807.10931, 0 | 6 | |
| Synthetic arabidopsis dataset D Ward, P Moghadam CSIRO. Data Collection., 2018 | 4 | 2018 |
| Using synthetic data to boost automated image-based plant phenotyping D Ward, P Moghadam, N Hudson | | |
Peyman MoghadamPrincipal Research Scientist, CSIRO | Professor (A), QUT在 csiro.au 的电子邮件经过验证
