2020 Publications

*bold denotes Core Centre Research Output

Abbasnejad, M. E., Shi, J., van den Hengel, A., & Liu, L. (2020). GADE: A Generative Adversarial Approach to Density Estimation and its Applications. International Journal of Computer Vision, 128(10–11), 2731–2743. https://doi.org/10.1007/s11263-020-01360-9

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

Adeli, V., Adeli, E., Reid, I., Niebles, J. C., & Rezatofighi, H. (2020). Socially and Contextually Aware Human Motion and Pose Forecasting. IEEE Robotics and Automation Letters, 5(4), 6033–6040. https://doi.org/10.1109/LRA.2020.3010742

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

Ali, S., Jonmohamadi, Y., Takeda, Y., Roberts, J., Crawford, R., & Pandey, A. K. (2020). Supervised Scene Illumination Control in Stereo Arthroscopes for Robot Assisted Minimally Invasive Surgery. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2020.3037301

Angelova, A., Carneiro, G., Sünderhauf, · Niko, Leitner, J., Sünderhauf, N., & Io, J. (2020). Special Issue on Deep Learning for Robotic Vision. International Journal of Computer Vision, 128, 1160–1161. https://doi.org/10.1007/s11263-020-01324-z

Antico, M., Sasazawa, F., Dunnhofer, M., Camps, S. M., Jaiprakash, A. T., Pandey, A. K., Crawford, R., Carneiro, G., & Fontanarosa, D. (2020). Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy. Ultrasound in Medicine and Biology, 46(2), 422–435. https://doi.org/10.1016/j.ultrasmedbio.2019.10.015

Antico, M., Sasazawa, F., Takeda, Y., Jaiprakash, A. T., Wille, M. L., Pandey, A. K., Crawford, R., & Fontanarosa, D. (2020). 4D Ultrasound-Based Knee Joint Atlas for Robotic Knee Arthroscopy: A Feasibility Study. IEEE Access, 8, 146331–146341. https://doi.org/10.1109/ACCESS.2020.3014999

Antico, M., Vukovic, D., Camps, S. M., Sasazawa, F., Takeda, Y., Le, A. T. H., Jaiprakash, A., Roberts, J., Crawford, R., Fontanarosa, D., & Carneiro, G. (2020). Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), 2543–2552. https://doi.org/10.1109/TUFFC.2020.2965291

Anwar, S., Khan, S., & Barnes, N. (2020). A Deep Journey into Super-resolution: A Survey. In ACM Computing Surveys (Vol. 53, Issue 3, pp. 1–34). Association for Computing Machinery. https://doi.org/10.1145/3390462

Arthur, J. N., Chaudhry, M. U., Woodruff, M. A., Pandey, A. K., & Yambem, S. D. (2020). Effect of Gate Conductance on Hygroscopic Insulator Organic Field‐Effect Transistors. Advanced Electronic Materials, 6(5), 1901079. https://doi.org/10.1002/aelm.201901079

Ayton, L. N., Barnes, N., Dagnelie, G., Fujikado, T., Goetz, G., Hornig, R., Jones, B. W., Muqit, M. M. K., Rathbun, D. L., Stingl, K., Weiland, J. D., & Petoe, M. A. (2020). An update on retinal prostheses. In Clinical Neurophysiology (Vol. 131, Issue 6, pp. 1383–1398). Elsevier Ireland Ltd. https://doi.org/10.1016/j.clinph.2019.11.029

Banach, A., Strydom, M., Jaiprakash, A., Carneiro, G., Brown, C., Crawford, R., & McFadyen, A. (2020). Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms. IEEE Access, 8, 213378–213388. https://doi.org/10.1109/ACCESS.2020.3040187

Blythe, R., O’Gorman, P. M., Crawford, R. W., Feenan, R., Hatton, A., Whitehouse, S. L., & Graves, N. (2020). Fixation Method for Hip Arthroplasty Stem Following Hip Fracture: A Population-Level Cost-Effectiveness Analysis. Journal of Arthroplasty, 35(6), 1614–1621. https://doi.org/10.1016/j.arth.2020.02.001

C. Santiago, C. Barata and M. Sasdelli et al., LOW: Training deep neural networks by learning optimal sample weights, Pattern Recognition, https://doi.org/10.1016/j.patcog.2020.107585

Calabro, L., Yong, M., Whitehouse, S. L., Hatton, A., de Steiger, R., & Crawford, R. W. (2020). Mortality and Implant Survival With Simultaneous and Staged Bilateral Total Hip Arthroplasty: Experience From the Australian Orthopedic Association National Joint Replacement Registry. Journal of Arthroplasty, 35(9), 2518–2524. https://doi.org/10.1016/j.arth.2020.04.027

Cao, J., Guo, Y., Wu, Q., Shen, C., Huang, J., & Tan, M. (2020). Improving Generative Adversarial Networks with Local Coordinate Coding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2020.3012096

Carneiro, G., Zorron Cheng Tao Pu, L., Singh, R., & Burt, A. (2020). Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy. Medical Image Analysis, 62, 101653. https://doi.org/10.1016/j.media.2020.101653

Chan, W. P., Pan, M. K. X. J., Croft, E. A., & Inaba, M. (2020). An Affordance and Distance Minimization Based Method for Computing Object Orientations for Robot Human Handovers. International Journal of Social Robotics, 12(1), 143–162. https://doi.org/10.1007/s12369-019-00546-7

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

Chen, Q., Wu, Q., Chen, J., Wu, Q., van den Hengel, A., & Tan, M. (2020). Scripted Video Generation With a Bottom-Up Generative Adversarial Network. IEEE Transactions on Image Processing, 29, 7454–7467. https://doi.org/10.1109/TIP.2020.3003227

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

Collins, J., McVicar, J., Wedlock, D., Brown, R., Howard, D., & Leitner, J. (2020). Benchmarking Simulated Robotic Manipulation through a Real World Dataset. IEEE Robotics and Automation Letters, 5(1), 250–257. https://doi.org/10.1109/LRA.2019.2953663

Coory, J. A., Tan, K. G., Whitehouse, S. L., Hatton, A., Graves, S. E., & Crawford, R. W. (2020). The Outcome of Total Knee Arthroplasty With and Without Patellar Resurfacing up to 17 Years: A Report From the Australian Orthopaedic Association National Joint Replacement Registry. Journal of Arthroplasty, 35(1), 132–138. https://doi.org/10.1016/j.arth.2019.08.007

Dai, Y., Lu, H., & Shen, C. (2020). Towards Light‐Weight Portrait Matting via Parameter Sharing. Computer Graphics Forum, cgf.14179. https://doi.org/10.1111/cgf.14179

de Bem, R., Ghosh, A., Ajanthan, T., Miksik, O., Boukhayma, A., Siddharth, N., & Torr, P. (2020). DGPose: Deep Generative Models for Human Body Analysis. International Journal of Computer Vision, 128(5), 1537–1563. https://doi.org/10.1007/s11263-020-01306-1

Deng, W., Zheng, L., Sun, Y., & Jiao, J. (2021). Rethinking Triplet Loss for Domain Adaptation. IEEE Transactions on Circuits and Systems for Video Technology, 31(1), 29–37. https://doi.org/10.1109/TCSVT.2020.2968484

Dhanani, J., Pang, G., Pincus, J., Ahern, B., Goodwin, W., Cowling, N., Whitten, G., Abdul-Aziz, M. H., Martin, S., Corke, P., & Laupland, K. B. (2020). Increasing ventilator surge capacity in COVID 19 pandemic: Design, manufacture and in vitro-in vivo testing in anaesthetized healthy pigs of a rapid prototyped mechanical ventilator. BMC Research Notes, 13(1), 1–6. https://doi.org/10.1186/s13104-020-05259-z

Dissanayake, T., Fernando, T., Denman, S., Ghaemmaghami, H., Sridharan, S., & Fookes, C. (2020). Domain Generalization in Biosignal Classification. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2020.3045720

Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Ghaemmaghami, H., & Fookes, C. (2020). A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation. IEEE Journal of Biomedical and Health Informatics, 1–1. https://doi.org/10.1109/jbhi.2020.3027910

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

Farazi, M. R., Khan, S. H., & Barnes, N. (2020). From known to the unknown: Transferring knowledge to answer questions about novel visual and semantic concepts. Image and Vision Computing, 103, 103985. https://doi.org/10.1016/j.imavis.2020.103985

faria, F. A., & Carneiro, G. (2020). Why are Generative Adversarial Networks so Fascinating and Annoying? 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 1–8. https://doi.org/10.1109/SIBGRAPI51738.2020.00009

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

Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2020). Deep Inverse Reinforcement Learning for Behavior Prediction in Autonomous Driving: Accurate Forecasts of Vehicle Motion. IEEE Signal Processing Magazine, 38(1), 87–96. https://doi.org/10.1109/MSP.2020.2988287

Fernando, T., Fookes, C., Denman, S., & Sridharan, S. (2020). Detection of Fake and Fraudulent Faces via Neural Memory Networks. IEEE Transactions on Information Forensics and Security, 16, 1973–1988. https://doi.org/10.1109/TIFS.2020.3047768

Fernando, T., Sridharan, S., McLaren, M., Priyasad, D., Denman, S., & Fookes, C. (2020). Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection. IEEE/ACM Transactions on Audio Speech and Language Processing, 28, 1159–1169. https://doi.org/10.1109/TASLP.2020.2982297

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

Fielding, A. L., Pandey, A. K., Jonmohamadi, Y., Via, R., Weber, D. C., Lomax, A. J., & Fattori, G. (2020). Preliminary study of the Intel RealSenseTM D415 camera for monitoring respiratory like motion of an irregular surface. IEEE Sensors Journal, 1–1. https://doi.org/10.1109/jsen.2020.2993264

Fischer, T., & Milford, M. (2020). Event-based visual place recognition with ensembles of temporal windows. IEEE Robotics and Automation Letters, 5(4), 6924–6931. https://doi.org/10.1109/LRA.2020.3025505

Ford, J. J., James, J., & Molloy, T. L. (2020). On the informativeness of measurements in Shiryaev’s Bayesian quickest change detection. Automatica, 111, 108645. https://doi.org/10.1016/j.automatica.2019.108645

Foster, A. L., Moriarty, T. F., Trampuz, A., Jaiprakash, A., Burch, M. A., Crawford, R., Paterson, D. L., Metsemakers, W-J., Schuetz, M., & Richards, R. G. (2020). Fracture-related infection: current methods for prevention and treatment. Expert Review of Anti-Infective Therapy, 18(4), 307–321. https://doi.org/10.1080/14787210.2020.1729740

Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2020). Fine-grained action segmentation using the semi-supervised action GAN. Pattern Recognition, 98, 107039. https://doi.org/10.1016/j.patcog.2019.107039

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

Garg, S., Harwood, B., Anand, G., & Milford, M. (2020). Delta Descriptors: Change-Based Place Representation for Robust Visual Localization. IEEE Robotics and Automation Letters, 5(4), 5120–5127. https://doi.org/10.1109/LRA.2020.3005627

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, 31(12), 5468–5482. https://doi.org/10.1109/TNNLS.2020.2968289

Gou, Y., Lei, Y., Liu, L., Zhang, P., & Peng, X. (2020). A Dynamic Parameter Enhanced Network for distant supervised relation extraction. Knowledge-Based Systems, 197, 105912. https://doi.org/10.1016/j.knosys.2020.105912

Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., & Yang, Y. (2020). Thorax disease classification with attention guided convolutional neural network. Pattern Recognition Letters, 131, 38–45. https://doi.org/10.1016/j.patrec.2019.11.040

Guo, Y., Chen, J., Du, Q., Van Den Hengel, A., Shi, Q., & Tan, M. (2020). Multi-way backpropagation for training compact deep neural networks. Neural Networks, 126, 250–261. https://doi.org/10.1016/j.neunet.2020.03.001

Han, K., Liu, M., Schnieders, D., & Wong, K. Y. K. (2021). Fixed Viewpoint Mirror Surface Reconstruction under an Uncalibrated Camera. IEEE Transactions on Image Processing, 30, 2141–2154. https://doi.org/10.1109/TIP.2021.3049946

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

He, T., Liu, Y., Shen, C., Wang, X., & Sun, C. (2020). Instance-Aware Embedding for Point Cloud Instance Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 12375 LNCS (pp. 255–270). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58577-8_16

Hernandez, V., Kulić, D., & Venture, G. (2020). Adversarial autoencoder for visualization and classification of human activity: Application to a low-cost commercial force plate. Journal of Biomechanics, 103, 109684. https://doi.org/10.1016/j.jbiomech.2020.109684

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

Hohwy, J., Hebblewhite, A., & Drummond, T. (2020). Events, Event Prediction, and Predictive Processing. Topics in Cognitive Science, tops.12491. https://doi.org/10.1111/tops.12491

Hojnik, T., Dungavell, R. A., Flick, P. D., & Roberts, J. M. (2020). Wheeled Rovers with Posable Hubs for Terrestrial and Extraterrestrial Exploration. IEEE Access, 8, 154318–154328. https://doi.org/10.1109/access.2020.3018429

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, 108858. https://doi.org/10.1016/j.automatica.2020.108858

Jacobson, A., Zeng, F., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2020). What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles. Journal of Field Robotics, rob.21978. https://doi.org/10.1002/rob.21978

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

Jawahar, C. V., Li, H., Mori, G., & Schindler, K. (2020, April 1). Guest Editorial: Special Issue on ACCV 2018. International Journal of Computer Vision, Vol. 128, p. 909. https://doi.org/10.1007/s11263-020-01296-0

Jayaratne, R., Kuhn, T., Christensen, B., Liu, X., Zing, I., Lamont, R., Dunbabin, M., Maddox, J., Fisher, G., & Morawska, L. (2020). Using a network of low-cost particle sensors to assess the impact of ship emissions on a residential community. Aerosol and Air Quality Research, 20(12), 2754–2764. https://doi.org/10.4209/aaqr.2020.06.0280

Jayaratne, R., Liu, X., Ahn, K. H., Asumadu-Sakyi, A., Fisher, G., Gao, J., Mabon, A., Mazaheri, M., Mullins, B., Nyaku, M., Ristovski, Z., Scorgie, Y., Thai, P., Dunbabin, M., & Morawska, L. (2020). Low-cost PM2.5 sensors: An assessment of their suitability for various applications. Aerosol and Air Quality Research, 20(3), 520–532. https://doi.org/10.4209/aaqr.2018.10.0390

Jonmohamadi, Y., Muthukumaraswamy, S., Chen, J., Roberts, J., Crawford, R., & Pandey, A. (2020). Extraction of Common Task Features in EEG-fMRI Data Using Coupled Tensor-Tensor Decomposition. Brain Topography, 33(5), 636–650. https://doi.org/10.1007/s10548-020-00787-0

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

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

Kielar, M., Gooch, H., Xu, L., Pandey, A. K., & Sah, P. (2021). Direct Detection of Neuronal Activity Using Organic Photodetectors. ACS Photonics, 8(1), 228–237. https://doi.org/10.1021/acsphotonics.0c01378

Kielar, M., Hamid, T., Wiemer, M., Windels, F., Hirsch, L., Sah, P., & Pandey, A. K. (2020). Light Detection in Open‐Circuit Voltage Mode of Organic Photodetectors. Advanced Functional Materials, 30(9), 1907964. https://doi.org/10.1002/adfm.201907964

Laurie, J., Higgins, N., Peynot, T., & Roberts, J. (2020). Dedicated Exposure Control for Remote Photoplethysmography. IEEE Access, 8, 116642–116652. https://doi.org/10.1109/ACCESS.2020.3003548

Lehnert, C., McCool, C., Corke, P., Sa, I., Stachniss, C., van Henten, E. J., & Nieto, J. (2020). Special issue on agricultural robotics. In Journal of Field Robotics (Vol. 37, Issue 1, pp. 5–6). John Wiley and Sons Inc. https://doi.org/10.1002/rob.21926

Lehnert, C., McCool, C., Sa, I., & Perez, T. (2020). Performance improvements of a sweet pepper harvesting robot in protected cropping environments. Journal of Field Robotics, rob.21973. https://doi.org/10.1002/rob.21973

Lehnert, C., McCool, C., Stachniss, C., Corke, P., Sa, I., Nieto, J., & Henten, E. J. (2020). JFR special issue on agricultural robotics, part 2. Journal of Field Robotics, 37(2), 185–186. https://doi.org/10.1002/rob.21939

Leitner, J., Morrison, D., Milan, A., Kelly-Boxall, N., Mctaggart, M., Tow, A. W., & Corke, P. (2020). Designing Cartman: A Cartesian Manipulator for the Amazon Robotics Challenge 2017. https://doi.org/10.1007/978-3-030-35679-8_11

Li, J., Liu, Y., Yuan, X., Zhao, C., Siegwart, R., Reid, I., & Cadena, C. (2020). Depth Based Semantic Scene Completion With Position Importance Aware Loss. IEEE Robotics and Automation Letters, 5(1), 219–226. https://doi.org/10.1109/LRA.2019.2953639

Liao, Z., Liu, L., Wu, Q., Teney, D., Shen, C., van den Hengel, A., & Verjans, J. (2020). Medical Data Inquiry Using a Question Answering Model. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020-April, 1490–1493. https://doi.org/10.1109/ISBI45749.2020.9098531

Liu, L., Cao, Z., Lu, H., Xiong, H., & Shen, C. (2020). NSSNet: Scale-Aware Object Counting With Non-Scale Suppression. IEEE Transactions on Intelligent Transportation Systems, 1–12. https://doi.org/10.1109/TITS.2020.3030781

Liu, W., Lin, G., Zhang, T., & Liu, Z. (2020). Guided Co-Segmentation Network for Fast Video Object Segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. https://doi.org/10.1109/tcsvt.2020.3010293

Liu, W., Zhang, P., Huang, X., Yang, J., Shen, C., & Reid, I. (2020). Real-time Image Smoothing via Iterative Least Squares. ACM Transactions on Graphics, 39(3), 1–24. https://doi.org/10.1145/3388887

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

Lu, X., Ma, C., Shen, J., Yang, X., Reid, I., & Yang, M.-H. (2020). Deep Object Tracking with Shrinkage Loss. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2020.3041332

Lv, K., Sheng, H., Xiong, Z., Li, W., & Zheng, L. (2020). Improving Driver Gaze Prediction with Reinforced Attention. IEEE Transactions on Multimedia. https://doi.org/10.1109/TMM.2020.3038311

Lv, K., Sheng, H., Xiong, Z., Li, W., & Zheng, L. (2020). Pose-Based View Synthesis for Vehicles: A Perspective Aware Method. IEEE Transactions on Image Processing, Vol. 29, pp. 5163–5174. https://doi.org/10.1109/TIP.2020.2980130

Mandel, N., Milford, M., & Gonzalez, F. (2020). A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs. Sensors, 20(16), 4420. https://doi.org/10.3390/s20164420

Mao, J., Hu, X., Zhang, L., He, X., & Milford, M. (2020). A Bio-Inspired Goal-Directed Visual Navigation Model for Aerial Mobile Robots. Journal of Intelligent and Robotic Systems: Theory and Applications, 100(1), 289–310. https://doi.org/10.1007/s10846-020-01190-4

McAuliffe, M. J., O’Connor, P. B., Major, L. J., Garg, G., Whitehouse, S. L., & Crawford, R. W. (2020). Highly Satisfied Total Knee Arthroplasty Patients Display a Wide Range of Soft Tissue Balance. Journal of Knee Surgery, 33(3), 247–254. https://doi.org/10.1055/s-0039-1677873

McAuliffe, M., O’Connor, P., Major, L., Garg, G., Whitehouse, S. L., & Crawford, R. (2020). Which Pre- and Postoperative Coronal Plane Laxity Parameters Influence Patient Satisfaction and Function after Primary Total Knee Arthroplasty? The Journal of Knee Surgery. https://doi.org/10.1055/s-0040-1710362

Meng, L., Lin, D., Francey, A., Gorbet, R., Beesley, P., & Kulić, D. (2020). Learning to Engage with Interactive Systems. ACM Transactions on Human-Robot Interaction, 10(1), 1–29. https://doi.org/10.1145/3408876

Milford, M. (2020). C. Elegans inspires self-driving cars. In Nature Machine Intelligence (Vol. 2, Issue 11, pp. 661–662). Nature Research. https://doi.org/10.1038/s42256-020-00245-3

Milford, M., Anthony, S., & Scheirer, W. (2020). Self-Driving Vehicles: Key Technical Challenges and Progress off the Road. IEEE Potentials, 39(1), 37–45. https://doi.org/10.1109/MPOT.2019.2939376

Molloy, T. L., Fischer, T., Milford, M., & Nair, G. N. (2020). Intelligent Reference Curation for Visual Place Recognition Via Bayesian Selective Fusion. IEEE Robotics and Automation Letters, 6(2), 588–595. https://doi.org/10.1109/LRA.2020.3047791

Molloy, T. L., Ford, J. J., & Perez, T. (2020). Online inverse optimal control for control-constrained discrete-time systems on finite and infinite horizons. Automatica, 120, 109109. https://doi.org/10.1016/j.automatica.2020.109109

Morrison, D., Corke, P., & Leitner, J. (2020). Learning robust, real-time, reactive robotic grasping. The International Journal of Robotics Research, 39(2–3), 183–201. https://doi.org/10.1177/0278364919859066

Morrison, D., Corke, P., Leitner, J., & Leitner, J. (2020). EGAD! An Evolved Grasping Analysis Dataset for Diversity and Reproducibility in Robotic Manipulation. IEEE Robotics and Automation Letters, 5(3), 4368–4375. https://doi.org/10.1109/LRA.2020.2992195

Mount, J., Xu, M., Dawes, L., & Milford, M. (2020). Unsupervised Selection of Optimal Operating Parameters for Visual Place Recognition Algorithms Using Gaussian Mixture Models. IEEE Robotics and Automation Letters, 6(2), 343–350. https://doi.org/10.1109/LRA.2020.3043171

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Vickers, M. L., Ballard, E. L., Harris, P. N. A., Knibbs, L. D., Jaiprakash, A., Dulhunty, J. M., Crawford, R., & 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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Ahmedt-Aristizabal, D., Armin, M. A., Denman, S., Fookes, C., & Petersson, L. (2020). Attention Networks for Multi-Task Signal Analysis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020-July, 184–187. https://doi.org/10.1109/EMBC44109.2020.9175730

Ahmedt-Aristizabal, D., Fernando, T., Denman, S., Petersson, L., Aburn, M. J., & Fookes, C. (2020). Neural Memory Networks for Seizure Type Classification. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020-July, 569–575. https://doi.org/10.1109/EMBC44109.2020.9175641

Akhter, I., Cheong, L. F., & Hartley, R. (2020). Fast postprocessing for difficult discrete energy minimization problems. Proceedings – 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 3462–3471. https://doi.org/10.1109/WACV45572.2020.9093369

Aliakbarian, S., Saleh, F. S., Salzmann, M., Petersson, L., & Gould, S. (2020). A stochastic conditioning scheme for diverse human motion prediction. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 5222–5231. https://doi.org/10.1109/CVPR42600.2020.00527

Ben-Shabat Y., Gould S. (2020) DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12346. Springer, Cham. https://doi.org/10.1007/978-3-030-58452-8_2

Cagara, D., Dunbabin, M., & Rigby, P. (2020). A Feature-Based Underwater Path Planning Approach using Multiple Perspective Prior Maps. Proceedings – IEEE International Conference on Robotics and Automation, 8573–8579. https://doi.org/10.1109/ICRA40945.2020.9196680

Campbell D., Liu L., Gould S. (2020) Solving the Blind Perspective-n-Point Problem End-to-End with Robust Differentiable Geometric Optimization. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12347. Springer, Cham. https://doi.org/10.1007/978-3-030-58536-5_15

Chan, W. P., Sakr, M., Quintero, C. P., Croft, E., & Van Der Loos, H. F. M. H. (2020). Towards a Multimodal System combining Augmented Reality and Electromyography for Robot Trajectory Programming and Execution. 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020, 419–424. https://doi.org/10.1109/RO-MAN47096.2020.9223526

Chancan, M., & Milford, M. (2020). CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning. Proceedings – IEEE International Conference on Robotics and Automation, 1697–1704. https://doi.org/10.1109/ICRA40945.2020.9197336

Chen, B., Parra, Á., Cao, J., Li, N., & Chin, T. J. (2020). End-to-end learnable geometric vision by backpropagating PNP optimization. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8097–8106. https://doi.org/10.1109/CVPR42600.2020.00812

Cheraghian, A., Rahman, S., Campbell, D., & Petersson, L. (2020). Transductive zero-shot learning for 3D point cloud classification. Proceedings – 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 912–922. https://doi.org/10.1109/WACV45572.2020.9093545

Coventry, J., Asselineau, C. A., Salahat, E., Raman, M. A., & Mahony, R. (2020). A robotic vision system for inspection of soiling at CSP plants. AIP Conference Proceedings, 2303(1), 100001. https://doi.org/10.1063/5.0029493

Dellaert F., Rosen D.M., Wu J., Mahony R., Carlone L. (2020) Shonan Rotation Averaging: Global Optimality by Surfing SO(p)n. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_18

Deremetz, M., Lenain, R., Laneurit, J., Debain, C., & Peynot, T. (2020). Autonomous human tracking using uwb sensors for mobile robots: An observer-based approach to follow the human path. CCTA 2020 – 4th IEEE Conference on Control Technology and Applications, 372–379. https://doi.org/10.1109/CCTA41146.2020.9206153

Ditria L., Meyer B.J., Drummond T. (2021) OpenGAN: Open Set Generative Adversarial Networks. In: Ishikawa H., Liu CL., Pajdla T., Shi J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science, vol 12625. Springer, Cham. https://doi.org/10.1007/978-3-030-69538-5_29

Du H., Yu X., Zheng L. (2020) Learning Object Relation Graph and Tentative Policy for Visual Navigation. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_2

Ehsanpour M., Abedin A., Saleh F., Shi J., Reid I., Rezatofighi H. (2020) Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12354. Springer, Cham. https://doi.org/10.1007/978-3-030-58545-7_11

Faisal, M., Akhter, I., Ali, M., & Hartley, R. (2020). EpO-Net: Exploiting geometric constraints on dense trajectories for motion saliency. Proceedings – 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 1873–1882. https://doi.org/10.1109/WACV45572.2020.9093589

Garg, S., & Milford, M. (2020). Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations. Proceedings – IEEE International Conference on Robotics and Automation, 3341–3348. https://doi.org/10.1109/ICRA40945.2020.9196827

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Hamaya, M., Lee, R., Tanaka, K., Von Drigalski, F., Nakashima, C., Shibata, Y., & Ijiri, Y. (2020). Learning Robotic Assembly Tasks with Lower Dimensional Systems by Leveraging Physical Softness and Environmental Constraints. Proceedings – IEEE International Conference on Robotics and Automation, 7747–7753. https://doi.org/10.1109/ICRA40945.2020.9197327

Hausler, S., & Milford, M. (2020). Hierarchical Multi-Process Fusion for Visual Place Recognition. Proceedings – IEEE International Conference on Robotics and Automation, 3327–3333. https://doi.org/10.1109/ICRA40945.2020.9197360

Henderson, J., Zamani, M., Mahony, R., & Trumpf, J. (2020). A Minimum Energy Filter for Localisation of an Unmanned Aerial Vehicle. Proceedings of the IEEE Conference on Decision and Control, 2020-December, 4188–4193. https://doi.org/10.1109/CDC42340.2020.9303730

Henein, M., Zhang, J., Mahony, R., & Ila, V. (2020). Dynamic SLAM: The Need for Speed. Proceedings – IEEE International Conference on Robotics and Automation, 2123–2129. https://doi.org/10.1109/ICRA40945.2020.9196895

Hojnik, T., Pond, L., Dungavell, R., Flick, P., & Roberts, J. (2020). Generating Locomotion with Effective Wheel Radius Manipulation. Proceedings – IEEE International Conference on Robotics and Automation, 2988–2994. https://doi.org/10.1109/ICRA40945.2020.9196825

Hou, Y., Zheng, L., & Gould, S. (2020). Learning to structure an image with few colors. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 10113–10122. https://doi.org/10.1109/CVPR42600.2020.01013

Hou Y., Zheng L., Gould S. (2020) Multiview Detection with Feature Perspective Transformation. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_1

James, J., Ford, J. J., & Molloy, T. L. (2020). A Novel Technique for Rejecting Non-Aircraft Artefacts in above Horizon Vision-Based Aircraft Detection. 2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020, 600–606. https://doi.org/10.1109/ICUAS48674.2020.9213938

Jiang, L., Gonzalez, F., & McFadyen, A. (2020). Cooperative Game Theory based Multi-UAV Consensus-based Formation Control. 2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020, 93–99. https://doi.org/10.1109/ICUAS48674.2020.9213841

Johnston, A., & Carneiro, G. (2020). Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 4755–4764. https://doi.org/10.1109/CVPR42600.2020.00481

Joo, K., Li, H., Oh, T. H., Bok, Y., & Kweon, I. S. (2020). Globally Optimal Relative Pose Estimation for Camera on a Selfie Stick. Proceedings – IEEE International Conference on Robotics and Automation, 4983–4989. https://doi.org/10.1109/ICRA40945.2020.9196921

Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2020). Semantic consistency and identity mapping multi-component generative adversarial network for person re-identification. Proceedings – 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 2256–2265. https://doi.org/10.1109/WACV45572.2020.9093323

Li D., Bak S., Bogomolov S. (2020) Reachability Analysis of Nonlinear Systems Using Hybridization and Dynamics Scaling. In: Bertrand N., Jansen N. (eds) Formal Modeling and Analysis of Timed Systems. FORMATS 2020. Lecture Notes in Computer Science, vol 12288. Springer, Cham. https://doi.org/10.1007/978-3-030-57628-8_16

Li, D., Opazo, C. R., Yu, X., & Li, H. (2020). Word-level deep sign language recognition from video: A new large-scale dataset and methods comparison. Proceedings – 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 1448–1458. https://doi.org/10.1109/WACV45572.2020.9093512

Li, D., Yu, X., Xu, C., Petersson, L., & Li, H. (2020). Transferring Cross-Domain Knowledge for Video Sign Language Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 6204–6213. https://doi.org/10.1109/CVPR42600.2020.00624

Liu F., Jonmohamadi Y., Maicas G., Pandey A.K., Carneiro G. (2020) Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12261. Springer, Cham. https://doi.org/10.1007/978-3-030-59710-8_58

Lu, Y., Valmadre, J., Wang, H., Kannala, J., Harandi, M., & Torr, P. H. S. (2020). Devon: Deformable volume network for learning optical flow. Proceedings – 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 2694–2702. https://doi.org/10.1109/WACV45572.2020.9093590

Lu, K., Barnes, N., Anwar, S., & Zheng, L. (2020). From depth what can you see? Depth completion via auxiliary image reconstruction. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 11303–11312. https://doi.org/10.1109/CVPR42600.2020.01132

Mahony, R., Van Goor, P., Henein, M., Pike, R., Zhang, J., & Ng, Y. (2020). Equivariant Visual Odometry in the Wild. Proceedings of the IEEE Conference on Decision and Control, 2020-December, 1314–1319. https://doi.org/10.1109/CDC42340.2020.9303824

Mandel, N., Alvarez, F. V., Milford, M., & Gonzalez, F. (2020, March 1). Towards Simulating Semantic Onboard UAV Navigation. IEEE Aerospace Conference Proceedings. https://doi.org/10.1109/AERO47225.2020.9172771

Mao W., Liu M., Salzmann M. (2020) History Repeats Itself: Human Motion Prediction via Motion Attention. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12359. Springer, Cham. https://doi.org/10.1007/978-3-030-58568-6_28

Mau, J., Devrelis, V., Day, G., Nash, G., Trumpf, J., & Delic, D. (2020). Through Thick and Thin: Imaging through Obscurant using SPAD array. Proceedings of IEEE Sensors, 2020-October. https://doi.org/10.1109/SENSORS47125.2020.9278706

Mau, J., Devrelis, V., Day, G., Trumpf, J., & Delic, D. V. (2020). The use of statistical mixture models to reduce noise in SPAD images of fog-obscured environments. In C. R. Valenta, J. A. Shaw, & M. Kimata (Eds.), SPIE Future Sensing Technologies (Vol. 11525, p. 20). https://doi.org/10.1117/12.2580251

McGee, J., Mathew, S. J., & Gonzalez, F. (2020). Unmanned Aerial Vehicle and Artificial Intelligence for Thermal Target Detection in Search and Rescue Applications. 2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020, 883–891. https://doi.org/10.1109/ICUAS48674.2020.9213849

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