| Object recognition datasets and challenges: A review A Salari, A Djavadifar, X Liu, H Najjaran Neurocomputing 495, 129-152, 2022 | 33 | 2022 |
| Automated visual detection of geometrical defects in composite manufacturing processes using deep convolutional neural networks A Djavadifar, JB Graham-Knight, M Kӧrber, P Lasserre, H Najjaran Journal of Intelligent Manufacturing 33 (8), 2257-2275, 2022 | 15 | 2022 |
| Segmentation of COVID-19 pneumonia lesions: A deep learning approach Z Ghomi, R Mirshahi, AK Bagheri, A Fattahpour, S Mohammadiun, ... Med J Islam Repub Iran 2020 (22), 174, 2020 | 10 | 2020 |
| Wrinkle and boundary detection of fiber products in robotic composites manufacturing K Gupta, M Körber, A Djavadifar, F Krebs, H Najjaran Assembly Automation 40 (2), 283-291, 2020 | 5 | 2020 |
| Robot-assisted composite manufacturing based on machine learning applied to multi-view computer vision A Djavadifar, JB Graham-Knight, K Gupta, M Körber, P Lasserre, ... Smart Multimedia: Second International Conference, ICSM 2019, San Diego, CA …, 2020 | 5 | 2020 |
| Accurate kidney segmentation in CT scans using deep transfer learning JB Graham-Knight, K Scotland, VKF Wong, A Djavadifar, D Lange, ... Smart Multimedia: Second International Conference, ICSM 2019, San Diego, CA …, 2020 | 4 | 2020 |
| Applying nnU-Net to the KiTS19 Grand Challenge JB Graham-Knight, A Djavadifar, P Lasserre, H Najjaran University of Minnesota Libraries Publishing, 2019 | 4 | 2019 |
| Boosted dense segmentation networks for constrained distributed systems JB Graham-Knight, A Djavadifar, H Najjaran, P Lasserre 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2021 | 2 | 2021 |
| Automatic detection of geometrical anomalies in composites manufacturing: a deep learning-based computer vision approach A Djavadifar University of British Columbia, 2020 | 1 | 2020 |
Homayoun NajjaranUniversity of VictoriaVerified email at uvic.ca
