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2020 Scientific Publications [10]

Hierarchical Attention Network for Action Segmentation

Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2020). Hierarchical Attention Network for Action Segmentation. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2020.01.023

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Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Park, C., Moghadam, P., Kim, S., Sridharan, S., & Fookes, C. (2020). Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach. IEEE Robotics and Automation Letters, 1–1. https://doi.org/10.1109/LRA.2020.2969164

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Bacterial Profile, Multi-Drug Resistance and Seasonality Following Lower Limb Orthopaedic Surgery in Tropical and Subtropical Australian Hospitals: An Epidemiological Cohort Study

Vickers, M. L., Ballard, E. L., Harris, P. N. A., Knibbs, L. D., Jaiprakash, A., Dulhunty, J. M., … Parkinson, B. (2020). Bacterial Profile, Multi-Drug Resistance and Seasonality Following Lower Limb Orthopaedic Surgery in Tropical and Subtropical Australian Hospitals: An Epidemiological Cohort Study. International Journal of Environmental Research and Public Health, 17(2), 657. https://doi.org/10.3390/ijerph17020657

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Deep learning for US image quality assessment based on femoral cartilage boundaries detection in autonomous knee arthroscopy

Antico, M., Fontanarosa, D., Carneiro, G., Vukovic, D., Camps, S. M., Sasazawa, F., … Crawford, R. (2020). Deep learning for US image quality assessment based on femoral cartilage boundaries detection in autonomous knee arthroscopy. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. https://doi.org/10.1109/TUFFC.2020.2965291

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Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations

Garg, S., & Milford, M. (2020). Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations. Retrieved from http://arxiv.org/abs/2001.08434

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Learning landmark guided embeddings for animal re-identification

Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2020). Learning landmark guided embeddings for animal re-identification. Retrieved from http://arxiv.org/abs/2001.02801

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What can robotics research learn from computer vision research?

Corke, P., Dayoub, F., Hall, D., Skinner, J., & Sünderhauf, N. (2020). What can robotics research learn from computer vision research? Retrieved from http://arxiv.org/abs/2001.02366

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Towards Surgical Robots: Understanding Interaction Challenges in Knee Surgery

Opie, J., Jaiprakash, A., Ploderer, B., Brereton, M., & Roberts, J. (2019). Towards Surgical Robots. Proceedings of the 31st Australian Conference on Human-Computer-Interaction, 255–265. https://doi.org/10.1145/3369457.3370916

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Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse Range Estimation

Arain, B., Dayoub, F., Rigby, P., & Dunbabin, M. (2020). Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse Range Estimation. Retrieved from http://arxiv.org/abs/2001.00330

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A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Hinas, A., Ragel, R., Roberts, J., & Gonzalez, F. (2020). A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS. Sensors, 20(1), 272. https://doi.org/10.3390/s20010272

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