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2020 Journal Articles [27]

Joint identification-verification for person re-identification: A four stream deep learning approach with improved quartet loss function

Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2020). Joint identification-verification for person re-identification: A four stream deep learning approach with improved quartet loss function. Computer Vision and Image Understanding, 102989. https://doi.org/10.1016/j.cviu.2020.102989

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Special Issue on Deep Learning for Robotic Vision

Angelova, A., Carneiro, G., Sünderhauf, N., & Leitner, J. (2020, May 1). Special Issue on Deep Learning for Robotic Vision. International Journal of Computer Vision. https://doi.org/10.1007/s11263-020-01324-z

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Haptics in Teleoperated Medical Interventions: Force Measurement, Haptic Interfaces and their Influence on User’s Performance

Abdi, E., Kulic, D., & Croft, E. (2020). Haptics in teleoperated medical interventions: Force measurement, haptic interfaces and their influence on users performance. IEEE Transactions on Biomedical Engineering, 1–1. https://doi.org/10.1109/tbme.2020.2987603

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Context from within: Hierarchical context modeling for semantic segmentation

Nguyen, K., Fookes, C., & Sridharan, S. (2020). Context from within: Hierarchical context modeling for semantic segmentation. Pattern Recognition, 105, 107358. https://doi.org/10.1016/j.patcog.2020.107358

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Neural Memory Plasticity for Medical Anomaly Detection

Fernando, T., Denman, S., Ahmedt-Aristizabal, D., Sridharan, S., Laurens, K. R., Johnston, P., & Fookes, C. (2020). Neural memory plasticity for medical anomaly detection. Neural Networks, 127, 67–81. https://doi.org/10.1016/j.neunet.2020.04.011

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Identification of Children At Risk of Schizophrenia via Deep Learning and EEG Responses

Ahmedt Aristizabal, D., Fernando, T., Denman, S., Robinson, J. E., Sridharan, S., Johnston, P. J., Laurens, K.R., & Fookes, C. (2020). Identification of Children At Risk of Schizophrenia via Deep Learning and EEG Responses. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2020.2984238

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Bio-inspired multi-scale fusion

Hausler, S., Chen, Z., Hasselmo, M. E., & Milford, M. (2020). Bio-inspired multi-scale fusion. Biological Cybernetics, 114(2), 209–229. https://doi.org/10.1007/s00422-020-00831-z

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Low-cost sensors as an alternative for long-term air quality monitoring

Liu, X., Jayaratne, R., Thai, P., Kuhn, T., Zing, I., Christensen, B., Lamont, R., Dunbabin, M., Zhu, S., Gao, J., Wainwright, D., Neale, D., Kan, R., Kirkwood, J., & Morawska, L. (2020). Low-cost sensors as an alternative for long-term air quality monitoring. Environmental Research, 185, 109438. https://doi.org/10.1016/j.envres.2020.109438

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High-Resolution Motor State Detection in Parkinson’s Disease Using Convolutional Neural Networks

Pfister, F. M. J., Um, T. T., Pichler, D. C., Goschenhofer, J., Abedinpour, K., Lang, M., Endo, S., Ceballos-Baumann, A. O., Hirche, S., Bischl, B., Kulić, D., & Fietzek, U. M. (2020). High-Resolution Motor State Detection in Parkinson’s Disease Using Convolutional Neural Networks. Scientific Reports, 10(1), 5860. https://doi.org/10.1038/s41598-020-61789-3

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Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy

"Gustavo Carneiro, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Alastair Burt, Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy, Medical Image Analysis,Volume 62,2020,101653,ISSN 1361-8415,https://doi.org/10.1016/j.media.2020.101653."

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Learning Deep Gradient Descent Optimization for Image Deconvolution

Gong, D., Zhang, Z., Shi, Q., van den Hengel, A., Shen, C., & Zhang, Y. (2020). Learning Deep Gradient Descent Optimization for Image Deconvolution. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2020.2968289

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Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images

Dunnhofer, M., Antico, M., Sasazawa, F., Takeda, Y., Camps, S., Martinel, N., Micheloni, C., Carneiro, G., & Fontanarosa, D. (2020). Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images. Medical Image Analysis, 60. https://doi.org/10.1016/j.media.2019.101631

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Nonlinear observer design on SL(3) for homography estimation by exploiting point and line correspondences with application to image stabilization

Hua, M. D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2020). Nonlinear observer design on SL(3) for homography estimation by exploiting point and line correspondences with application to image stabilization. Automatica, 115, 1–10.

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Low-cost PM2. 5 Sensors: An Assessment of Their Suitability for Various Applications

Jayaratne, R., Liu, X., Ahn, K.-H., Asumadu-Sakyi, A., Fisher, G., Gao, J., Mabon, A., Mazaheri, M., Mullins, B., Nyaku, M., Ristovki, Z., Scorgie, Y., Thai, P., Dunbabin, M., & Morawska, L. (2020). Low-cost PM 2.5 Sensors: An Assessment of their Suitability for Various Applications. Aerosol and Air Quality Research, 20, 520–532. https://doi.org/10.4209/aaqr.2018.10.0390

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Dietary Saturated Fatty Acids Modulate Pain Behaviour in Trauma-Induced Osteoarthritis in Rats

Sekar, S., Panchal, S. K., Ghattamaneni, N. K., Brown, L., Crawford, R., Xiao, Y., & Prasadam, I. (2020). Dietary Saturated Fatty Acids Modulate Pain Behaviour in Trauma-Induced Osteoarthritis in Rats. Nutrients, 12(2), 509. https://doi.org/10.3390/nu12020509

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Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning

Jonmohamadi, Y., Takeda, Y., Liu, F., Sasazawa, F., Maicas, G., Crawford, R., Roberts, J., Pandey, A.K., & Carneiro, G. (2020). Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning. IEEE Access, 1–1. https://doi.org/10.1109/access.2020.2980025

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Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Schaffter, T., Buist, D. S. M., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., … Jung, H. (2020). Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Network Open, 3(3), e200265. https://doi.org/10.1001/jamanetworkopen.2020.0265

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LSTM guided ensemble correlation filter tracking with appearance model pool

Jain, M., Subramanyam, A. V., Denman, S., Sridharan, S., & Fookes, C. (2020). LSTM guided ensemble correlation filter tracking with appearance model pool. Computer Vision and Image Understanding, 195, 102935. https://doi.org/10.1016/j.cviu.2020.102935

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A Hybrid Compact Neural Architecture for Visual Place Recognition

Chancan, M., Hernandez-Nunez, L., Narendra, A., Barron, A. B., & Milford, M. (2020). A Hybrid compact neural architecture for visual place recognition. IEEE Robotics and Automation Letters, 5(2), 993–1000. https://doi.org/10.1109/LRA.2020.2967324

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Exploring Performance Bounds of Visual Place Recognition Using Extended Precision

Ferrarini, B., Waheed, M., Waheed, S., Ehsan, S., Milford, M. J., & McDonald-Maier, K. D. (2020). Exploring performance bounds of visual place recognition using extended precision. IEEE Robotics and Automation Letters, 5(2), 1688–1695. https://doi.org/10.1109/LRA.2020.2969197

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CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments

Zaffar, M., Ehsan, S., Milford, M., & McDonald-Maier, K. (2020). CoHOG: A light-weight, compute-efficient, and training-free visual place recognition technique for changing environments. IEEE Robotics and Automation Letters, 5(2), 1835–1842. https://doi.org/10.1109/LRA.2020.2969917

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Model-free vision-based shaping of deformable plastic materials

Cherubini, A., Ortenzi, V., Cosgun, A., Lee, R., & Corke, P. (2020). Model-free vision-based shaping of deformable plastic materials. The International Journal of Robotics Research, 027836492090768. https://doi.org/10.1177/0278364920907684

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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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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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