2017 Publications

Book Section

  • Observers for Position Estimation Using Bearing and Biased Velocity Information

    *Hamel, T., Mahony, R., & Samson, C. (2017). Observers for Position Estimation Using Bearing and Biased Velocity Information. In T. I. F. (3), K. Y. P. (4), & H. N. (5) (Eds.), Sensing and Control for Autonomous Vehicles (Volume 474, pp. 3–23). Springer International Publishing. https://doi.org/10.1007/978-3-319-55372-6_1

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Scientific Publications

  • Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor

    Stoffregen, T., & Kleeman, L. (2017). Simultaneous optical flow and segmentation (SOFAS) using dynamic vision sensor. Australasian Conference on Robotics and Automation, ACRA, 2017-December, 52–61.

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  • Towards Context-Aware Interaction Recognition for Visual Relationship Detection

    Zhuang, B., Liu, L., Shen, C., & Reid, I. (2017). Towards Context-Aware Interaction Recognition for Visual Relationship Detection. Proceedings of the IEEE International Conference on Computer Vision, 2017-October, 589–598. https://doi.org/10.1109/ICCV.2017.71

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  • Semi-dense visual odometry for RGB-D cameras using approximate nearest neighbour fields

    Zhou, Y., Kneip, L., & Li, H. (2017). Semi-dense visual odometry for RGB-D cameras using approximate nearest neighbour fields. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6261–6268). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989742

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  • Accurate extrinsic calibration between monocular camera and sparse 3D Lidar points without markers

    Xiao, Z., Li, H., Zhou, D., Dai, Y., & Dai, B. (2017). Accurate extrinsic calibration between monocular camera and sparse 3D Lidar points without markers. In 2017 IEEE Intelligent Vehicles Symposium (IV) (pp. 424–429). Los Angeles, CA, USA: IEEE. http://doi.org/10.1109/IVS.2017.7995755

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  • Multi-attention Network for One Shot Learning

    Wang, P., Liu, L., Shen, C., Huang, Z., Hengel, A. van den, & Shen, H. T. (2017). Multi-attention Network for One Shot Learning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6212–6220). Honolulu, HI, USA: IEEE. http://doi.org/10.1109/CVPR.2017.658

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  • Joint pose and principal curvature refinement using quadrics

    Spek, A., & Drummond, T. (2017). Joint pose and principal curvature refinement using quadrics. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3968–3975). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989456

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  • CREST: Convolutional Residual Learning for Visual Tracking

    Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R. W. H., & Yang, M.-H. (2017). CREST: Convolutional Residual Learning for Visual Tracking. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2574–2583). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.279

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  • Encouraging LSTMs to Anticipate Actions Very Early

    Aliakbarian, M. S., Saleh, F. S., Salzmann, M., Fernando, B., Petersson, L., & Andersson, L. (2017). Encouraging LSTMs to Anticipate Actions Very Early. Proceedings of the IEEE International Conference on Computer Vision, 2017-October, 280–289. https://doi.org/10.1109/ICCV.2017.39

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  • CNN-based small object detection and visualization with feature activation mapping

    Menikdiwela, M., Nguyen, C., Li, H., & Shaw, M. (2018). CNN-based small object detection and visualization with feature activation mapping. International Conference Image and Vision Computing New Zealand, 2017-December, 1–5. https://doi.org/10.1109/IVCNZ.2017.8402455

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  • Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior

    Maicas, G., Carneiro, G., & Bradley, A. P. (2017). Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (pp. 305–309). Melbourne, Australia: IEEE. http://doi.org/10.1109/ISBI.2017.7950525

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  • When Unsupervised Domain Adaptation Meets Tensor Representations

    Lu, H., Zhang, L., Cao, Z., Wei, W., Xian, K., Shen, C., & Hengel, A. van den. (2017). When Unsupervised Domain Adaptation Meets Tensor Representations. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 599–608). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.72

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  • Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map

    Liu, L., Li, H., & Dai, Y. (2017). Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2391–2400). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.260

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  • Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks

    Li, H., Wang, P., & Shen, C. (2017). Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 5248–5256). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.560

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  • RATSAC – Random Tree Sampling for Maximum Consensus Estimation

    Le, H., Chin, T.-J., & Suter, D. (2017). RATSAC - Random Tree Sampling for Maximum Consensus Estimation. In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–8). Sydney, Australia: IEEE. http://doi.org/10.1109/DICTA.2017.8227480

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  • An Exact Penalty Method for Locally Convergent Maximum Consensus

    Le, H., Chin, T.-J., & Suter, D. (2017). An Exact Penalty Method for Locally Convergent Maximum Consensus. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 379–387). Honolulu, USA: IEEE. http://doi.org/10.1109/CVPR.2017.48

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  • Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames

    Kumar, S., Dai, Y., & Li, H. (2017). Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 4659–4667). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.498

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  • RRD-SLAM: Radial-distorted rolling-shutter direct SLAM

    Kim, J.-H., Latif, Y., & Reid, I. (2017). RRD-SLAM: Radial-distorted rolling-shutter direct SLAM. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5148–5154). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989602

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  • A discrete-time attitude observer on SO(3) for vision and GPS fusion

    Khosravian, A., Chin, T.-J., Reid, I., & Mahony, R. (2017). A discrete-time attitude observer on SO(3) for vision and GPS fusion. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5688–5695). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989669

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  • Scaling CNNs for High Resolution Volumetric Reconstruction From a Single Image

    Johnston, A., Garg, R., Carneiro, G., Reid, I., & Van Den Hengel, A. (2017). Scaling CNNs for High Resolution Volumetric Reconstruction from a Single Image. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 2018-January, 930–939. https://doi.org/10.1109/ICCVW.2017.114

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  • Deep Subspace Clustering Networks

    Ji, P., Zhang, T., Li, H., & Salzmann EPFL -CVLab Ian Reid, M. (2017). Deep Subspace Clustering Networks. In 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach, CA, USA. Retrieved from http://papers.nips.cc/paper/6608-deep-subspace-clustering-networks.pdf

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  • “Maximizing Rigidity” Revisited: A Convex Programming Approach for Generic 3D Shape Reconstruction from Multiple Perspective Views

    Ji, P., Li, H., Dai, Y., & Reid, I. (2017). “Maximizing Rigidity” Revisited: A Convex Programming Approach for Generic 3D Shape Reconstruction from Multiple Perspective Views. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 929–937). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.106

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  • Fast Incremental Bundle Adjustment with Covariance Recovery

    Ila, V., Polok, L., Solony, M., & Istenic, K. (2018). Fast incremental bundle adjustment with covariance recovery. Proceedings - 2017 International Conference on 3D Vision, 3DV 2017, 175–184. https://doi.org/10.1109/3DV.2017.00029

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  • Point and line feature-based observer design on SL(3) for Homography estimation and its application to image stabilization

    Hua, M.-D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2017). Point and line feature-based observer design on SL(3) for Homography estimation and its application to image stabilization. In International Conference on Robotics and Automation (ICRA). Singapore. Retrieved from https://hal.archives-ouvertes.fr/hal-01628175/

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  • Explicit Complementary Observer Design on Special Linear Group SL(3) for Homography Estimation using Conic Correspondences

    Hua, M. D., Hamel, T., Mahony, R., & Allibert, G. (2018). Explicit complementary observer design on Special Linear Group SL(3) for homography estimation using conic correspondences. 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, 2018-January, 2434–2441. https://doi.org/10.1109/CDC.2017.8264006

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  • Exploring The Effect of Meta-Structural Information on the Global Consistency of SLAM

    Henein, M., Abello, M., Ila, V., & Mahony, R. (2017). Exploring the effect of meta-structural information on the global consistency of SLAM. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 1616–1623. https://doi.org/10.1109/IROS.2017.8205970

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  • Smart Mining for Deep Metric Learning

    Harwood, B., G, V. K. B., Carneiro, G., Reid, I., & Drummond, T. (2017). Smart Mining for Deep Metric Learning. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2840–2848). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.307

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  • Self-Paced Kernel Estimation for Robust Blind Image Deblurring

    Gong, D., Tan, M., Zhang, Y., Hengel, A. van den, & Shi, Q. (2017). Self-Paced Kernel Estimation for Robust Blind Image Deblurring. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 1670–1679). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.184

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  • Pose Changes From a Different Point of View

    Chirikjian, G. S., Mahony, R., Ruan, S., & Trumpf, J. (2017). Pose Changes From a Different Point of View. In ASME 2017 41st Mechanisms and Robotics Conference. Cleveland, Ohio: ASME. http://doi.org/10.1115/DETC2017-67725

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  • Generalized Rank Pooling for Activity Recognition

    Cherian, A., Fernando, B., Harandi, M., & Gould, S. (2017). Generalized Rank Pooling for Activity Recognition. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1581–1590). Honolulu, HI, USA: IEEE. http://doi.org/10.1109/CVPR.2017.172

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  • Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence

    Campbell, D., Petersson, L., Kneip, L., & Li, H. (2017). Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 1–10). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.10

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  • Visual Question Answering: A Tutorial

    Teney, D., Wu, Q., & van den Hengel, A. (2017). Visual Question Answering: A Tutorial. IEEE Signal Processing Magazine, 34(6), 63–75. https://doi.org/10.1109/MSP.2017.2739826

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  • Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

    Ren, C. Y., Prisacariu, V. A., Kähler, O., Reid, I. D., Murray, D. W. (2017). Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions. International Journal of Computer Vision, 124, 80–95. https://doi.org/10.1007/s11263-016-0978-2

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  • Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework

    Oakden-Rayner, L., Carneiro, G., Bessen, T., Nascimento, J. C., Bradley, A. P., & Palmer, L. J. (2017). Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Scientific Reports, 7(1), 1648. http://doi.org/10.1038/s41598-017-01931-w

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  • Deep Learning on Sparse Manifolds for Faster Object Segmentation

    Nascimento, J. C., & Carneiro, G. (2017). Deep Learning on Sparse Manifolds for Faster Object Segmentation. IEEE Transactions on Image Processing, 26(10), 4978–4990. http://doi.org/10.1109/TIP.2017.2725582

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  • Learning a no-reference quality metric for single-image super-resolution

    Ma, C., Yang, C.-Y., Yang, X., & Yang, M.-H. (2017). Learning a no-reference quality metric for single-image super-resolution. Computer Vision and Image Understanding, 158, 1–16. http://doi.org/10.1016/J.CVIU.2016.12.009

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  • TasselNet: counting maize tassels in the wild via local counts regression network

    Lu, H., Cao, Z., Xiao, Y., Zhuang, B., & Shen, C. (2017). TasselNet: counting maize tassels in the wild via local counts regression network. Plant Methods, 13(1), 79. http://doi.org/10.1186/s13007-017-0224-0

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  • Cross-Convolutional-Layer Pooling for Image Recognition

    Liu, L., Shen, C., & Hengel, A. van den. (2017). Cross-Convolutional-Layer Pooling for Image Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2305–2313. http://doi.org/10.1109/TPAMI.2016.2637921

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  • Sparse optimization for robust and efficient loop closing

    Latif, Y., Huang, G., Leonard, J., & Neira, J. (2017). Sparse optimization for robust and efficient loop closing. Robotics and Autonomous Systems, 93, 13–26. http://doi.org/10.1016/J.ROBOT.2017.03.016

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  • Efficient guided hypothesis generation for multi-structure epipolar geometry estimation

    Lai, T., Wang, H., Yan, Y., Xiao, G., & Suter, D. (2017). Efficient guided hypothesis generation for multi-structure epipolar geometry estimation. Computer Vision and Image Understanding, 154, 152–165. http://doi.org/10.1016/J.CVIU.2016.10.003

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  • Compressed fusion of GNSS and inertial navigation with simultaneous localization and mapping

    Kim, J., Cheng, J., Guivant, J., & Nieto, J. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localization and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36. https://doi.org/10.1109/MAES.2017.8071552

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  • Enhancing Feature Discrimination for Unsupervised Hashing

    Hoang, T., Do, T. T., Le Tan, D. K., & Cheung, N. M. (2018). Enhancing feature discrimination for unsupervised hashing. Proceedings - International Conference on Image Processing, ICIP, 2017-September, 3710–3714. https://doi.org/10.1109/ICIP.2017.8296975

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  • Discriminatively Learned Hierarchical Rank Pooling Networks

    Fernando, B., & Gould, S. (2017). Discriminatively Learned Hierarchical Rank Pooling Networks. International Journal of Computer Vision, 124(3), 335–355. http://doi.org/10.1007/s11263-017-1030-x

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  • Learning Camera Pose from Optical Colonoscopy Frames Through Deep Convolutional Neural Network (CNN)

    Armin M.A., Barnes N., Alvarez J., Li H., Grimpen F., Salvado O. (2017) Learning Camera Pose from Optical Colonoscopy Frames Through Deep Convolutional Neural Network (CNN). In: Cardoso M. et al. (eds) Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures. CARE 2017, CLIP 2017. Lecture Notes in Computer Science, vol 10550. Springer, Cham. https://doi.org/10.1007/978-3-319-67543-5_5

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  • Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination

    Zhang, F., Leitner, J., Milford, M., & Corke, P. I. (2017). Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017-July, 496–497. https://doi.org/10.1109/CVPRW.2017.74

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  • Action recognition: From static datasets to moving robots

    Rezazadegan, F., Shirazi, S., Upcrofit, B., & Milford, M. (2017). Action recognition: From static datasets to moving robots. Proceedings - IEEE International Conference on Robotics and Automation, 3185–3191. https://doi.org/10.1109/ICRA.2017.7989361

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  • Long Range Iris Recognition: A Survey

    Nguyen, K., Fookes, C., Jillela, R., Sridharan, S., & Ross, A. (2017). Long Range Iris Recognition: A Survey. Pattern Recognition. http://doi.org/10.1016/j.patcog.2017.05.021

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  • 3D tracking of water hazards with polarized stereo cameras

    Nguyen, C. V., Milford, M., & Mahony, R. (2017). 3D tracking of water hazards with polarized stereo cameras. Proceedings - IEEE International Conference on Robotics and Automation, 5251–5257. https://doi.org/10.1109/ICRA.2017.7989616

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  • Detection of Aircraft Below The Horizon for Vision-Based Detect And Avoid in Unmanned Aircraft Systems

    Molloy, Timothy L., Ford, Jason J., & Mejias, L. (2017). Detection of Aircraft Below The Horizon for Vision-Based Detect And Avoid in Unmanned Aircraft Systems. Journal of Field Robotics. http://doi.org/10.1002/rob.21719

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  • Minimax Robust Quickest Change Detection With Exponential Delay Penalties

    Molloy, T. L., Kennedy, J. M., & Ford, J. J. (2017). Minimax Robust Quickest Change Detection With Exponential Delay Penalties. IEEE Control Systems Letters, 1(2), 280–285. http://doi.org/10.1109/LCSYS.2017.2714262

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  • Inverse Noncooperative Dynamic Games

    Molloy, T. L., Ford, J. J., & Perez, T. (2017). Inverse Noncooperative Dynamic Games. IFAC-PapersOnLine, 50(1), 11788–11793. https://doi.org/10.1016/j.ifacol.2017.08.1989

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  • Towards Unsupervised Weed Scouting for Agricultural Robotics

    Hall, D., Dayoub, F., Kulk, J., & McCool, C. (2017). Towards unsupervised weed scouting for agricultural robotics. Proceedings - IEEE International Conference on Robotics and Automation, 5223–5230. https://doi.org/10.1109/ICRA.2017.7989612

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  • Improving condition- and environment-invariant place recognition with semantic place categorization

    Garg, S., Jacobson, A., Kumar, S., & Milford, M. (2017). Improving condition- and environment-invariant place recognition with semantic place categorization. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 6863–6870. https://doi.org/10.1109/IROS.2017.8206608

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  • Trajectory tracking passivity-based control for marine vehicles subject to disturbances

    Donaire, A., Romero, J. G., & Perez, T. (2017). Trajectory tracking passivity-based control for marine vehicles subject to disturbances. Journal of the Franklin Institute, 354(5), 2167–2182. http://doi.org/10.1016/j.jfranklin.2017.01.012

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  • Episode-Based Active Learning with Bayesian Neural Networks

    Dayoub, F., Sunderhauf, N., & Corke, P. I. (2017). Episode-Based Active Learning with Bayesian Neural Networks. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017-July, 498–500. https://doi.org/10.1109/CVPRW.2017.75

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  • A behaviour tree-based robust decision framework for enhanced UAV autonomy

    Crofts, D., Bruggemann, T. S., & Ford, J. J. (2017). A behaviour tree-based robust decision framework for enhanced UAV autonomy. In 17th Australian International Aerospace Congress (AIAC17). Melbourne, Victoria. Retrieved from http://eprints.qut.edu.au/106017/

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  • Deep learning features at scale for visual place recognition

    Chen, Z., Jacobson, A., Sunderhauf, N., Upcroft, B., Liu, L., Shen, C., Reid, I., & Milford, M. (2017). Deep learning features at scale for visual place recognition. Proceedings - IEEE International Conference on Robotics and Automation, 3223–3230. https://doi.org/10.1109/ICRA.2017.7989366

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  • A Transplantable System for Weed Classification by Agricultural Robotics

    Hall, D., Dayoub, F., Perez, T., & McCool, C. (2017). A transplantable system for weed classification by agricultural robotics. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 5174–5179. https://doi.org/10.1109/IROS.2017.8206406

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  • Meaningful maps with object-oriented semantic mapping

    Sunderhauf, N., Pham, T. T., Latif, Y., Milford, M., & Reid, I. (2017). Meaningful maps with object-oriented semantic mapping. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 5079–5085. https://doi.org/10.1109/IROS.2017.8206392

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  • Unsupervised Human Action Detection by Action Matching

    Fernando, B., Shirazi, S., & Gould, S. (2017). Unsupervised Human Action Detection by Action Matching. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017-July, 1604–1612. https://doi.org/10.1109/CVPRW.2017.205

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  • Neural Aggregation Network for Video Face Recognition

    Yang, J., Ren, P., Zhang, D., Chen, D., Wen, F., Li, H., & Hua, G. (2017). Neural aggregation network for video face recognition. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 5216–5225. https://doi.org/10.1109/CVPR.2017.554

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  • Guided Open Vocabulary Image Captioning with Constrained Beam Search.

    Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2017). Guided Open Vocabulary Image Captioning with Constrained Beam Search. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 936–945. https://doi.org/10.18653/v1/D17-1098

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  • Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters

    Drory, A., Zhu, G., Li, H., & Hartley, R. (2017). Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters. Computer Vision and Image Understanding, 159, 116–127. https://doi.org/10.1016/j.cviu.2016.12.002

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  • The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions

    Wang, P., Wu, Q., Shen, C., & Van Den Hengel, A. (2017). The VQA-machine: Learning how to use existing vision algorithms to answer new questions. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 3909–3918. https://doi.org/10.1109/CVPR.2017.416

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  • Sequential Person Recognition in Photo Albums with a Recurrent Network

    Li, Y., Lin, G., Zhuang, B., Liu, L., Shen, C., & Van Den Hengel, A. (2017). Sequential person recognition in photo albums with a recurrent network. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 5660–5668. https://doi.org/10.1109/CVPR.2017.600

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  • RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

    Lin, G., Milan, A., Shen, C., & Reid, I. (2017). RefineNet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 5168–5177. https://doi.org/10.1109/CVPR.2017.549

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  • Infinite Variational Autoencoder for Semi-Supervised Learning

    Abbasnejad, M. E., Dick, A., & Van Den Hengel, A. (2017). Infinite variational autoencoder for semi-supervised learning. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 781–790. https://doi.org/10.1109/CVPR.2017.90

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  • Graph-Structured Representations for Visual Question Answering

    Teney, D., Liu, L., & Van Den Hengel, A. (2017). Graph-structured representations for visual question answering. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 3233–3241. https://doi.org/10.1109/CVPR.2017.344

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  • Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation

    Cao, Y., Shen, C., & Shen, H. T. (2017). Exploiting depth from single monocular images for object detection and semantic segmentation. IEEE Transactions on Image Processing, 26(2), 836–846. https://doi.org/10.1109/TIP.2016.2621673

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  • DeepSetNet: Predicting Sets with Deep Neural Networks

    Rezatofighi, S. H., Vijay Kumar, B. G., Milan, A., Abbasnejad, E., Dick, A., & Reid, I. (2017). DeepSetNet: Predicting Sets with Deep Neural Networks. Proceedings of the IEEE International Conference on Computer Vision, 2017-October, 5257–5266. https://doi.org/10.1109/ICCV.2017.561

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  • Attend in groups: a weakly-supervised deep learning framework for learning from web data

    Zhuang, B., Liu, L., Li, Y., Shen, C., & Reid, I. (2017). Attend in groups: A weakly-supervised deep learning framework for learning from web data. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 2915–2924. https://doi.org/10.1109/CVPR.2017.311

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  • FPGA acceleration of multilevel ORB feature extraction for computer vision

    Weberruss, J., Kleeman, L., Boland, D., & Drummond, T. (2017). FPGA acceleration of multilevel ORB feature extraction for computer vision. In 2017 27th International Conference on Field Programmable Logic and Applications (FPL) (pp. 1–8). Ghent, Belgium: IEEE. http://doi.org/10.23919/FPL.2017.8056856

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  • Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation

    Zuo, Y., & Drummond, T. (2017). Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation. In Proceedings of the 1st Annual Conference on Robot Learning, in PMLR 78 (pp. 27–36). Retrieved from http://proceedings.mlr.press/v78/zuo17a.html

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  • A compact parametric solution to depth sensor calibration

    Spek, A., Drummond, T. (2017) A compact parametric solution to depth sensor calibration. In 28th British Machine Vision Conference (BMVC). London: https://bmvc2017.london/proceedings/

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  • Thrust Control for Multirotor Aerial Vehicles

    Bangura, M., & Mahony, R. (2017). Thrust Control for Multirotor Aerial Vehicles. IEEE Transactions on Robotics, 33(2), 390–405. http://doi.org/10.1109/TRO.2016.2633562

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  • Modular Design of Image Based Visual Servo Control for Dynamic Mechanical Systems

    Mahony, R. (2017). Modular Design of Image Based Visual Servo Control for Dynamic Mechanical Systems. In Robotics Research (pp. 129–146). Springer International Publishing. http://doi.org/10.1007/978-3-319-29363-9_8

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  • Spatio-temporal union of subspaces for multi-body non-rigid structure-from-motion

    Kumar, S., Dai, Y., & Li, H. (2017). Spatio-temporal union of subspaces for multi-body non-rigid structure-from-motion. Pattern Recognition, 71, 428–443. http://doi.org/10.1016/J.PATCOG.2017.05.014

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  • Joint Dimensionality Reduction and Metric Learning: A Geometric Take

    Harandi, M., Salzmann, M., & Hartley, R. (2017). Joint Dimensionality Reduction and Metric Learning: A Geometric Take. In Proceedings of the 34th International Conference on Machine Learning (ICML). Sydney, Australia.

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  • Determination of the vertical profile of particle number concentration adjacent to a motorway using an unmanned aerial vehicle

    Villa, T. F., Jayaratne, E. R., Gonzalez, L. F., & Morawska, L. (2017). Determination of the vertical profile of particle number concentration adjacent to a motorway using an unmanned aerial vehicle. Environmental Pollution, 230, 134–142. http://doi.org/10.1016/j.envpol.2017.06.033

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  • Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery

    Sandino, J., Wooler, A., & Gonzalez, F. (2017). Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery. Sensors, 17(10), 2196. http://doi.org/10.3390/s17102196

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  • Change Detection for Undermodelled Processes Using Mismatched Hidden Markov Model Test Filters

    James, J., Ford, J. J., & Molloy, T. L. (2017). Change Detection for Undermodelled Processes Using Mismatched Hidden Markov Model Test Filters. IEEE Control Systems Letters, 1(2), 238–243. http://doi.org/10.1109/LCSYS.2017.2713825

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  • Quickest detection of intermittent signals with estimated anomaly times

    James, J., Ford, J. J., & Molloy, T. L. (2018). Quickest detection of intermittent signals with estimated anomaly times. 2017 Asian Control Conference, ASCC 2017, 2018-January, 2066–2070. https://doi.org/10.1109/ASCC.2017.8287493

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  • Going deeper: Autonomous steering with neural memory networks. In IEEE Conference on Computer Vision and Pattern Recognition

    Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2017). Going deeper: Autonomous steering with neural memory networks. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 2018-January, 214–221. https://doi.org/10.1109/ICCVW.2017.34

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  • Combining Line Segments and Points for Appearance- based Indoor Navigation by Image Based Visual Servoing

    Bista, S. R., Giordano, P. R., & Chaumette, F. (2017). Combining line segments and points for appearance-based indoor navigation by image based visual servoing. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 2960–2967. https://doi.org/10.1109/IROS.2017.8206131

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  • Evaluation of Keypoint Detectors and Descriptors in Arthroscopic Images for Feature-Based Matching Applications

    Marmol, A., Peynot, T., Eriksson, A., Jaiprakash, A., Roberts, J., & Crawford, R. (2017). Evaluation of Keypoint Detectors and Descriptors in Arthroscopic Images for Feature-Based Matching Applications. IEEE Robotics and Automation Letters, 2(4), 2135–2142. http://doi.org/10.1109/LRA.2017.2714150

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  • Vision-Based Target Finding and Inspection of a Ground Target Using a Multirotor UAV System

    Hinas, A., Roberts, J., & Gonzalez, F. (2017). Vision-Based Target Finding and Inspection of a Ground Target Using a Multirotor UAV System. Sensors, 17(12), 2929. http://doi.org/10.3390/s17122929

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  • Robot for weed species plant-specific management

    Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., Lehnert, C., and Perez, T. (2017). Robot for weed species plant-specific management. Journal of Field Robotics, 34(6), 1179–1199. http://doi.org/10.1002/rob.21727

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  • Biologically-inspired visual place recognition with adaptive multiple scales

    Fan, C., Chen, Z., Jacobson, A., Hu, X., & Milford, M. (2017). Biologically-inspired visual place recognition with adaptive multiple scales. Robotics and Autonomous Systems, 96, 224–237. http://doi.org/10.1016/J.ROBOT.2017.07.015

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  • The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research

    Leitner, J., Tow, A. W., Sunderhauf, N., Dean, J. E., Durham, J. W., Cooper, M., … Corke, P. (2017). The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 4705–4712). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989545

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  • Improved Semantic segmentation for robotic applications with hierarchical conditional random fields

    Meyer, B. J., & Drummond, T. (2017). Improved semantic segmentation for robotic applications with hierarchical conditional random fields. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5258–5265). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989617

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  • Solving Robust Regularization Problems Using Iteratively Re-weighted Least Squares

    Kiani, K. A., & Drummond, T. (2017). Solving Robust Regularization Problems Using Iteratively Re-weighted Least Squares. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 483–492). Santa Rosa, CA: IEEE. http://doi.org/10.1109/WACV.2017.60

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  • Face identity recognition in simulated prosthetic vision is poorer than previously reported and can be improved by caricaturing

    *Irons, J. L., Gradden, T., Zhang, A., He, X., Barnes, N., Scott, A. F., & McKone, E. (2017). Face identity recognition in simulated prosthetic vision is poorer than previously reported and can be improved by caricaturing. Vision Research, 137, 61–79. https://doi.org/10.1016/j.visres.2017.06.002

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  • Determining the Contribution of Retinotopic Discrimination to Localization Performance With a Suprachoroidal Retinal Prosthesis

    *Petoe, M. A., McCarthy, C. D., Shivdasani, M. N., Sinclair, N. C., Scott, A. F., Ayton, L. N., … Blamey, P. J. (2017). Determining the Contribution of Retinotopic Discrimination to Localization Performance With a Suprachoroidal Retinal Prosthesis. Investigative Opthalmology & Visual Science, 58(7), 3231. https://doi.org/10.1167/iovs.16-21041

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  • Training Improves Vibrotactile Spatial Acuity and Intensity Discrimination on the Lower Back Using Coin Motors

    Stronks, H. C., Walker, J., Parker, D. J., & Barnes, N. (2017). Training Improves Vibrotactile Spatial Acuity and Intensity Discrimination on the Lower Back Using Coin Motors. Artificial Organs. https://doi.org/10.1111/aor.12882

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  • Higher-Order Pooling of CNN Features via Kernel Linearization for Action Recognition

    Cherian, A., Koniusz, P., & Gould, S. (2017). Higher-Order Pooling of CNN Features via Kernel Linearization for Action Recognition. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 130–138). Santa Rosa, CA: IEEE. http://doi.org/10.1109/WACV.2017.22

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  • Ordered Pooling of Optical Flow Sequences for Action Recognition

    Wang, J., Cherian, A., & Porikli, F. (2017). Ordered Pooling of Optical Flow Sequences for Action Recognition. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 168–176). Santa Rosa, CA: IEEE. http://doi.org/10.1109/WACV.2017.26

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  • SLAM++ -A highly efficient and temporally scalable incremental SLAM framework

    Ila, V., Polok, L., Solony, M., & Svoboda, P. (2017). SLAM++ -A highly efficient and temporally scalable incremental SLAM framework. The International Journal of Robotics Research, 36(2), 210–230. http://doi.org/10.1177/0278364917691110

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  • A learning-based markerless approach for full-body kinematics estimation in-natura from a single image

    Drory, A., Li, H., & Hartley, R. (2017). A learning-based markerless approach for full-body kinematics estimation in-natura from a single image. Journal of Biomechanics, 55, 1–10. http://doi.org/10.1016/j.jbiomech.2017.01.028

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  • Convergence and State Reconstruction of Time-Varying Multi-Agent Systems From Complete Observability Theory

    *Anderson, B. D. O., Shi, G., & Trumpf, J. (2017). Convergence and State Reconstruction of Time-Varying Multi-Agent Systems From Complete Observability Theory. IEEE Transactions on Automatic Control, 62(5), 2519–2523. http://doi.org/10.1109/TAC.2016.2599274

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  • A converse to the deterministic separation principle

    Trumpf, J., & Trentelman, H. L. (2017). A converse to the deterministic separation principle. Systems & Control Letters, 101, 2–9. http://doi.org/10.1016/j.sysconle.2016.02.021

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  • Dense monocular reconstruction using surface normals

    Weerasekera, C. S., Latif, Y., Garg, R., & Reid, I. (2017). Dense monocular reconstruction using surface normals. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2524–2531). IEEE. https://doi.org/10.1109/ICRA.2017.7989293

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  • An Analytic Approach to Converting POE Parameters Into D–H Parameters for Serial-Link Robots

    Wu, L., Crawford, R., & Roberts, J. (2017). An Analytic Approach to Converting POE Parameters Into D–H Parameters for Serial-Link Robots. IEEE Robotics and Automation Letters, 2(4), 2174–2179. http://doi.org/10.1109/LRA.2017.2723470

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  • Standard operating procedures for UAV or drone basedmonitoring of wildlife

    Gonzalez, F., & Johnson, S. (2017). Standard operating procedures for UAV or drone basedmonitoring of wildlife. In Proceedings of Unmanned Aircraft Systems for Remote Sensing) UAS4RS 2017. Hobart, Tasmania. Retrieved from https://eprints.qut.edu.au/108859/

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  • Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs

    Dharmasiri, T., Spek, A., & Drummond, T. (2017). Joint prediction of depths, normals and surface curvature from RGB images using CNNs. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1505–1512). Vancouver, Canada: IEEE. http://doi.org/10.1109/IROS.2017.8205954

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  • Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment

    Bruce, J., Jacobson, A., & Milford, M. (2017). Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment. IEEE Robotics and Automation Letters, 2(4), 2209–2216. http://doi.org/10.1109/LRA.2017.2724146

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  • A vision-based sense-and-avoid system tested on a ScanEagle UAV

    Bratanov, D., Mejias, L., & Ford, J. J. (2017). A vision-based sense-and-avoid system tested on a ScanEagle UAV. 2017 International Conference on Unmanned Aircraft Systems, ICUAS 2017, 1134–1142. https://doi.org/10.1109/ICUAS.2017.7991302

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  • Rank Pooling for Action Recognition

    Fernando, B., Gavves, E., Oramas M., J. O., Ghodrati, A., & Tuytelaars, T. (2017). Rank Pooling for Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 773–787. http://doi.org/10.1109/TPAMI.2016.2558148

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  • Estimating the projected frontal surface area of cyclists from images using a variational framework and statistical shape and appearance models

    Drory, A., Li, H., & Hartley, R. (2017). Estimating the projected frontal surface area of cyclists from images using a variational framework and statistical shape and appearance models. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology. https://doi.org/10.1177/1754337117705489

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  • Robotics, Vision and Control : Fundamental Algorithms in MATLAB® (2nd ed.).

    Corke, P. I. (2017). Robotics, Vision and Control : Fundamental Algorithms in MATLAB® (2nd ed.). Springer International Publishing.

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  • Observers for Position Estimation Using Bearing and Biased Velocity Information

    *Hamel, T., Mahony, R., & Samson, C. (2017). Observers for Position Estimation Using Bearing and Biased Velocity Information. In T. I. F. (3), K. Y. P. (4), & H. N. (5) (Eds.), Sensing and Control for Autonomous Vehicles (Volume 474, pp. 3–23). Springer International Publishing. https://doi.org/10.1007/978-3-319-55372-6_1

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  • A Deep Convolutional Neural Network Module that Promotes Competition of Multiple-size Filters

    Liao, Z., & Carneiro, G. (2017). A Deep Convolutional Neural Network Module that Promotes Competition of Multiple-size Filters. Pattern Recognition. http://doi.org/10.1016/j.patcog.2017.05.024 *In Press

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  • Introduction to the special section on Artificial Intelligence and Computer Vision

    Lu, H., Guna, J., & Dansereau, D. G. (2017). Introduction to the special section on Artificial Intelligence and Computer Vision. Computers & Electrical Engineering, 58, 444–446. http://doi.org/10.1016/j.compeleceng.2017.04.024

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  • Kinematic comparison of surgical tendon-driven manipulators and concentric tube manipulators

    Li, Z., Wu, L., Ren, H., & Yu, H. (2017). Kinematic comparison of surgical tendon-driven manipulators and concentric tube manipulators. Mechanism and Machine Theory, 107, 148–165. http://doi.org/10.1016/j.mechmachtheory.2016.09.018

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  • Finding the Kinematic Base Frame of a Robot by Hand-Eye Calibration Using 3D Position Data

    Wu, L., & Ren, H. (2017). Finding the Kinematic Base Frame of a Robot by Hand-Eye Calibration Using 3D Position Data. IEEE Transactions on Automation Science and Engineering, 14(1), 314–324. http://doi.org/10.1109/TASE.2016.2517674

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  • Autonomous Sweet Pepper Harvesting for Protected Cropping Systems

    Lehnert, C., English, A., McCool, C., Tow, A. W., & Perez, T. (2017). Autonomous Sweet Pepper Harvesting for Protected Cropping Systems. IEEE Robotics and Automation Letters, 2(2), 872–879. http://doi.org/10.1109/LRA.2017.2655622

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  • Optical-Aided Aircraft Navigation using Decoupled Visual SLAM with Range Sensor Augmentation

    Andert, F., Ammann, N., Krause, S., Lorenz, S., Bratanov, D., & Mejias, L. (2017). Optical-Aided Aircraft Navigation using Decoupled Visual SLAM with Range Sensor Augmentation. Journal of Intelligent & Robotic Systems, 1–19. http://doi.org/10.1007/s10846-016-0457-6

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  • Coregistered Hyperspectral and Stereo Image Seafloor Mapping from an Autonomous Underwater Vehicle

    Bongiorno, D. L., Bryson, M., Bridge, T. C. L., Dansereau, D. G., & Williams, S. B. (2017). Coregistered Hyperspectral and Stereo Image Seafloor Mapping from an Autonomous Underwater Vehicle. Journal of Field Robotics. http://doi.org/10.1002/rob.21713

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  • Mixtures of Lightweight Deep Convolutional Neural Networks: Applied to Agricultural Robotics

    McCool, C., Perez, T., & Upcroft, B. (2017). Mixtures of Lightweight Deep Convolutional Neural Networks: Applied to Agricultural Robotics. IEEE Robotics and Automation Letters, 2(3), 1344–1351. http://doi.org/10.1109/LRA.2017.2667039

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  • Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting—Combined Color and 3-D Information

    Sa, I., Lehnert, C., English, A., McCool, C., Dayoub, F., Upcroft, B., & Perez, T. (2017). Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting—Combined Color and 3-D Information. IEEE Robotics and Automation Letters, 2(2), 765–772. http://doi.org/10.1109/LRA.2017.2651952

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  • Dexterity Analysis of Three 6-DOF Continuum Robots Combining Concentric Tube Mechanisms and Cable-Driven Mechanisms

    Wu, L., Crawford, R., & Roberts, J. (2017). Dexterity Analysis of Three 6-DOF Continuum Robots Combining Concentric Tube Mechanisms and Cable-Driven Mechanisms. IEEE Robotics and Automation Letters, 2(2), 514–521. http://doi.org/10.1109/LRA.2016.2645519

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  • Orthopaedic surgeon attitudes towards current limitations and the potential for robotic and technological innovation in arthroscopic surgery

    Jaiprakash, A., O’Callaghan, W. B., Whitehouse, S. L., Pandey, A., Wu, L., Roberts, J., & Crawford, R. W. (2017). Orthopaedic surgeon attitudes towards current limitations and the potential for robotic and technological innovation in arthroscopic surgery. Journal of Orthopaedic Surgery, 25(1), 230949901668499. http://doi.org/10.1177/2309499016684993

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  • Farm Workers of the Future: Vision-Based Robotics for Broad-Acre Agriculture

    Ball, D., Ross, P., English, A., Milani, P., Richards, D., Bate, A., Upcroft, B., Wyeth, G., Corke, P. (2017). Farm Workers of the Future: Vision-Based Robotics for Broad-Acre Agriculture. IEEE Robotics & Automation Magazine, 1–1. http://doi.org/10.1109/MRA.2016.2616541

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  • Image-Based Visual Servoing With Unknown Point Feature Correspondence

    McFadyen, A., Jabeur, M., & Corke, P. (2017). Image-Based Visual Servoing With Unknown Point Feature Correspondence. IEEE Robotics and Automation Letters, 2(2), 601–607. http://doi.org/10.1109/LRA.2016.2645886

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  • Image-Based Visual Servoing With Light Field Cameras

    Tsai, D., Dansereau, D. G., Peynot, T., & Corke, P. (2017). Image-Based Visual Servoing With Light Field Cameras. IEEE Robotics and Automation Letters, 2(2), 912–919. http://doi.org/10.1109/LRA.2017.2654544

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  • Self-Supervised Video Representation Learning With Odd-One-Out Networks

    Fernando, B., Bilen, H., Gavves, E., & Gould, S. (2017). Self-supervised video representation learning with odd-one-out networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 5729–5738. https://doi.org/10.1109/CVPR.2017.607

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  • Spherepix: A Data Structure for Spherical Image Processing

    Adarve, J. D., & Mahony, R. (2017). Spherepix: A Data Structure for Spherical Image Processing. IEEE Robotics and Automation Letters, 2(2), 483–490. http://doi.org/10.1109/LRA.2016.2645119

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  • Motion Segmentation Via a Sparsity Constraint

    Lai, T., Wang, H., Yan, Y., Chin, T.-J., & Zhao, W.-L. (2016). Motion Segmentation Via a Sparsity Constraint. IEEE Transactions on Intelligent Transportation Systems, PP(99), 1–11. http://doi.org/10.1109/TITS.2016.2596296 *Article in press

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  • Clustering with Hypergraphs: The Case for Large Hyperedges

    Purkait, P., Chin, T. J., Sadri, A., & Suter, D. (2017). Clustering with Hypergraphs: The Case for Large Hyperedges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(9), 1697–1711. https://doi.org/10.1109/TPAMI.2016.2614980

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Books

  • Robotics, Vision and Control : Fundamental Algorithms in MATLAB® (2nd ed.).

    Corke, P. I. (2017). Robotics, Vision and Control : Fundamental Algorithms in MATLAB® (2nd ed.). Springer International Publishing.

    View More

Journal Articles

  • Visual Question Answering: A Tutorial

    Teney, D., Wu, Q., & van den Hengel, A. (2017). Visual Question Answering: A Tutorial. IEEE Signal Processing Magazine, 34(6), 63–75. https://doi.org/10.1109/MSP.2017.2739826

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  • Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

    Ren, C. Y., Prisacariu, V. A., Kähler, O., Reid, I. D., Murray, D. W. (2017). Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions. International Journal of Computer Vision, 124, 80–95. https://doi.org/10.1007/s11263-016-0978-2

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  • Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework

    Oakden-Rayner, L., Carneiro, G., Bessen, T., Nascimento, J. C., Bradley, A. P., & Palmer, L. J. (2017). Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Scientific Reports, 7(1), 1648. http://doi.org/10.1038/s41598-017-01931-w

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  • Deep Learning on Sparse Manifolds for Faster Object Segmentation

    Nascimento, J. C., & Carneiro, G. (2017). Deep Learning on Sparse Manifolds for Faster Object Segmentation. IEEE Transactions on Image Processing, 26(10), 4978–4990. http://doi.org/10.1109/TIP.2017.2725582

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  • Learning a no-reference quality metric for single-image super-resolution

    Ma, C., Yang, C.-Y., Yang, X., & Yang, M.-H. (2017). Learning a no-reference quality metric for single-image super-resolution. Computer Vision and Image Understanding, 158, 1–16. http://doi.org/10.1016/J.CVIU.2016.12.009

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  • TasselNet: counting maize tassels in the wild via local counts regression network

    Lu, H., Cao, Z., Xiao, Y., Zhuang, B., & Shen, C. (2017). TasselNet: counting maize tassels in the wild via local counts regression network. Plant Methods, 13(1), 79. http://doi.org/10.1186/s13007-017-0224-0

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  • Cross-Convolutional-Layer Pooling for Image Recognition

    Liu, L., Shen, C., & Hengel, A. van den. (2017). Cross-Convolutional-Layer Pooling for Image Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2305–2313. http://doi.org/10.1109/TPAMI.2016.2637921

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  • Sparse optimization for robust and efficient loop closing

    Latif, Y., Huang, G., Leonard, J., & Neira, J. (2017). Sparse optimization for robust and efficient loop closing. Robotics and Autonomous Systems, 93, 13–26. http://doi.org/10.1016/J.ROBOT.2017.03.016

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  • Efficient guided hypothesis generation for multi-structure epipolar geometry estimation

    Lai, T., Wang, H., Yan, Y., Xiao, G., & Suter, D. (2017). Efficient guided hypothesis generation for multi-structure epipolar geometry estimation. Computer Vision and Image Understanding, 154, 152–165. http://doi.org/10.1016/J.CVIU.2016.10.003

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  • Compressed fusion of GNSS and inertial navigation with simultaneous localization and mapping

    Kim, J., Cheng, J., Guivant, J., & Nieto, J. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localization and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36. https://doi.org/10.1109/MAES.2017.8071552

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  • Discriminatively Learned Hierarchical Rank Pooling Networks

    Fernando, B., & Gould, S. (2017). Discriminatively Learned Hierarchical Rank Pooling Networks. International Journal of Computer Vision, 124(3), 335–355. http://doi.org/10.1007/s11263-017-1030-x

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  • Long Range Iris Recognition: A Survey

    Nguyen, K., Fookes, C., Jillela, R., Sridharan, S., & Ross, A. (2017). Long Range Iris Recognition: A Survey. Pattern Recognition. http://doi.org/10.1016/j.patcog.2017.05.021

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  • Detection of Aircraft Below The Horizon for Vision-Based Detect And Avoid in Unmanned Aircraft Systems

    Molloy, Timothy L., Ford, Jason J., & Mejias, L. (2017). Detection of Aircraft Below The Horizon for Vision-Based Detect And Avoid in Unmanned Aircraft Systems. Journal of Field Robotics. http://doi.org/10.1002/rob.21719

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  • Minimax Robust Quickest Change Detection With Exponential Delay Penalties

    Molloy, T. L., Kennedy, J. M., & Ford, J. J. (2017). Minimax Robust Quickest Change Detection With Exponential Delay Penalties. IEEE Control Systems Letters, 1(2), 280–285. http://doi.org/10.1109/LCSYS.2017.2714262

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  • Inverse Noncooperative Dynamic Games

    Molloy, T. L., Ford, J. J., & Perez, T. (2017). Inverse Noncooperative Dynamic Games. IFAC-PapersOnLine, 50(1), 11788–11793. https://doi.org/10.1016/j.ifacol.2017.08.1989

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  • Trajectory tracking passivity-based control for marine vehicles subject to disturbances

    Donaire, A., Romero, J. G., & Perez, T. (2017). Trajectory tracking passivity-based control for marine vehicles subject to disturbances. Journal of the Franklin Institute, 354(5), 2167–2182. http://doi.org/10.1016/j.jfranklin.2017.01.012

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  • Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters

    Drory, A., Zhu, G., Li, H., & Hartley, R. (2017). Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters. Computer Vision and Image Understanding, 159, 116–127. https://doi.org/10.1016/j.cviu.2016.12.002

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  • Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation

    Cao, Y., Shen, C., & Shen, H. T. (2017). Exploiting depth from single monocular images for object detection and semantic segmentation. IEEE Transactions on Image Processing, 26(2), 836–846. https://doi.org/10.1109/TIP.2016.2621673

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  • Thrust Control for Multirotor Aerial Vehicles

    Bangura, M., & Mahony, R. (2017). Thrust Control for Multirotor Aerial Vehicles. IEEE Transactions on Robotics, 33(2), 390–405. http://doi.org/10.1109/TRO.2016.2633562

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  • Spatio-temporal union of subspaces for multi-body non-rigid structure-from-motion

    Kumar, S., Dai, Y., & Li, H. (2017). Spatio-temporal union of subspaces for multi-body non-rigid structure-from-motion. Pattern Recognition, 71, 428–443. http://doi.org/10.1016/J.PATCOG.2017.05.014

    View More

  • Determination of the vertical profile of particle number concentration adjacent to a motorway using an unmanned aerial vehicle

    Villa, T. F., Jayaratne, E. R., Gonzalez, L. F., & Morawska, L. (2017). Determination of the vertical profile of particle number concentration adjacent to a motorway using an unmanned aerial vehicle. Environmental Pollution, 230, 134–142. http://doi.org/10.1016/j.envpol.2017.06.033

    View More

  • Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery

    Sandino, J., Wooler, A., & Gonzalez, F. (2017). Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery. Sensors, 17(10), 2196. http://doi.org/10.3390/s17102196

    View More

  • Change Detection for Undermodelled Processes Using Mismatched Hidden Markov Model Test Filters

    James, J., Ford, J. J., & Molloy, T. L. (2017). Change Detection for Undermodelled Processes Using Mismatched Hidden Markov Model Test Filters. IEEE Control Systems Letters, 1(2), 238–243. http://doi.org/10.1109/LCSYS.2017.2713825

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  • Evaluation of Keypoint Detectors and Descriptors in Arthroscopic Images for Feature-Based Matching Applications

    Marmol, A., Peynot, T., Eriksson, A., Jaiprakash, A., Roberts, J., & Crawford, R. (2017). Evaluation of Keypoint Detectors and Descriptors in Arthroscopic Images for Feature-Based Matching Applications. IEEE Robotics and Automation Letters, 2(4), 2135–2142. http://doi.org/10.1109/LRA.2017.2714150

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  • Vision-Based Target Finding and Inspection of a Ground Target Using a Multirotor UAV System

    Hinas, A., Roberts, J., & Gonzalez, F. (2017). Vision-Based Target Finding and Inspection of a Ground Target Using a Multirotor UAV System. Sensors, 17(12), 2929. http://doi.org/10.3390/s17122929

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  • Robot for weed species plant-specific management

    Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., Lehnert, C., and Perez, T. (2017). Robot for weed species plant-specific management. Journal of Field Robotics, 34(6), 1179–1199. http://doi.org/10.1002/rob.21727

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  • Biologically-inspired visual place recognition with adaptive multiple scales

    Fan, C., Chen, Z., Jacobson, A., Hu, X., & Milford, M. (2017). Biologically-inspired visual place recognition with adaptive multiple scales. Robotics and Autonomous Systems, 96, 224–237. http://doi.org/10.1016/J.ROBOT.2017.07.015

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  • Face identity recognition in simulated prosthetic vision is poorer than previously reported and can be improved by caricaturing

    *Irons, J. L., Gradden, T., Zhang, A., He, X., Barnes, N., Scott, A. F., & McKone, E. (2017). Face identity recognition in simulated prosthetic vision is poorer than previously reported and can be improved by caricaturing. Vision Research, 137, 61–79. https://doi.org/10.1016/j.visres.2017.06.002

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  • Determining the Contribution of Retinotopic Discrimination to Localization Performance With a Suprachoroidal Retinal Prosthesis

    *Petoe, M. A., McCarthy, C. D., Shivdasani, M. N., Sinclair, N. C., Scott, A. F., Ayton, L. N., … Blamey, P. J. (2017). Determining the Contribution of Retinotopic Discrimination to Localization Performance With a Suprachoroidal Retinal Prosthesis. Investigative Opthalmology & Visual Science, 58(7), 3231. https://doi.org/10.1167/iovs.16-21041

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  • Training Improves Vibrotactile Spatial Acuity and Intensity Discrimination on the Lower Back Using Coin Motors

    Stronks, H. C., Walker, J., Parker, D. J., & Barnes, N. (2017). Training Improves Vibrotactile Spatial Acuity and Intensity Discrimination on the Lower Back Using Coin Motors. Artificial Organs. https://doi.org/10.1111/aor.12882

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  • SLAM++ -A highly efficient and temporally scalable incremental SLAM framework

    Ila, V., Polok, L., Solony, M., & Svoboda, P. (2017). SLAM++ -A highly efficient and temporally scalable incremental SLAM framework. The International Journal of Robotics Research, 36(2), 210–230. http://doi.org/10.1177/0278364917691110

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  • A learning-based markerless approach for full-body kinematics estimation in-natura from a single image

    Drory, A., Li, H., & Hartley, R. (2017). A learning-based markerless approach for full-body kinematics estimation in-natura from a single image. Journal of Biomechanics, 55, 1–10. http://doi.org/10.1016/j.jbiomech.2017.01.028

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  • Convergence and State Reconstruction of Time-Varying Multi-Agent Systems From Complete Observability Theory

    *Anderson, B. D. O., Shi, G., & Trumpf, J. (2017). Convergence and State Reconstruction of Time-Varying Multi-Agent Systems From Complete Observability Theory. IEEE Transactions on Automatic Control, 62(5), 2519–2523. http://doi.org/10.1109/TAC.2016.2599274

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  • A converse to the deterministic separation principle

    Trumpf, J., & Trentelman, H. L. (2017). A converse to the deterministic separation principle. Systems & Control Letters, 101, 2–9. http://doi.org/10.1016/j.sysconle.2016.02.021

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  • An Analytic Approach to Converting POE Parameters Into D–H Parameters for Serial-Link Robots

    Wu, L., Crawford, R., & Roberts, J. (2017). An Analytic Approach to Converting POE Parameters Into D–H Parameters for Serial-Link Robots. IEEE Robotics and Automation Letters, 2(4), 2174–2179. http://doi.org/10.1109/LRA.2017.2723470

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  • Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment

    Bruce, J., Jacobson, A., & Milford, M. (2017). Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment. IEEE Robotics and Automation Letters, 2(4), 2209–2216. http://doi.org/10.1109/LRA.2017.2724146

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  • Rank Pooling for Action Recognition

    Fernando, B., Gavves, E., Oramas M., J. O., Ghodrati, A., & Tuytelaars, T. (2017). Rank Pooling for Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 773–787. http://doi.org/10.1109/TPAMI.2016.2558148

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  • Estimating the projected frontal surface area of cyclists from images using a variational framework and statistical shape and appearance models

    Drory, A., Li, H., & Hartley, R. (2017). Estimating the projected frontal surface area of cyclists from images using a variational framework and statistical shape and appearance models. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology. https://doi.org/10.1177/1754337117705489

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  • A Deep Convolutional Neural Network Module that Promotes Competition of Multiple-size Filters

    Liao, Z., & Carneiro, G. (2017). A Deep Convolutional Neural Network Module that Promotes Competition of Multiple-size Filters. Pattern Recognition. http://doi.org/10.1016/j.patcog.2017.05.024 *In Press

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  • Kinematic comparison of surgical tendon-driven manipulators and concentric tube manipulators

    Li, Z., Wu, L., Ren, H., & Yu, H. (2017). Kinematic comparison of surgical tendon-driven manipulators and concentric tube manipulators. Mechanism and Machine Theory, 107, 148–165. http://doi.org/10.1016/j.mechmachtheory.2016.09.018

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  • Finding the Kinematic Base Frame of a Robot by Hand-Eye Calibration Using 3D Position Data

    Wu, L., & Ren, H. (2017). Finding the Kinematic Base Frame of a Robot by Hand-Eye Calibration Using 3D Position Data. IEEE Transactions on Automation Science and Engineering, 14(1), 314–324. http://doi.org/10.1109/TASE.2016.2517674

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  • Autonomous Sweet Pepper Harvesting for Protected Cropping Systems

    Lehnert, C., English, A., McCool, C., Tow, A. W., & Perez, T. (2017). Autonomous Sweet Pepper Harvesting for Protected Cropping Systems. IEEE Robotics and Automation Letters, 2(2), 872–879. http://doi.org/10.1109/LRA.2017.2655622

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  • Optical-Aided Aircraft Navigation using Decoupled Visual SLAM with Range Sensor Augmentation

    Andert, F., Ammann, N., Krause, S., Lorenz, S., Bratanov, D., & Mejias, L. (2017). Optical-Aided Aircraft Navigation using Decoupled Visual SLAM with Range Sensor Augmentation. Journal of Intelligent & Robotic Systems, 1–19. http://doi.org/10.1007/s10846-016-0457-6

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  • Coregistered Hyperspectral and Stereo Image Seafloor Mapping from an Autonomous Underwater Vehicle

    Bongiorno, D. L., Bryson, M., Bridge, T. C. L., Dansereau, D. G., & Williams, S. B. (2017). Coregistered Hyperspectral and Stereo Image Seafloor Mapping from an Autonomous Underwater Vehicle. Journal of Field Robotics. http://doi.org/10.1002/rob.21713

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  • Mixtures of Lightweight Deep Convolutional Neural Networks: Applied to Agricultural Robotics

    McCool, C., Perez, T., & Upcroft, B. (2017). Mixtures of Lightweight Deep Convolutional Neural Networks: Applied to Agricultural Robotics. IEEE Robotics and Automation Letters, 2(3), 1344–1351. http://doi.org/10.1109/LRA.2017.2667039

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  • Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting—Combined Color and 3-D Information

    Sa, I., Lehnert, C., English, A., McCool, C., Dayoub, F., Upcroft, B., & Perez, T. (2017). Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting—Combined Color and 3-D Information. IEEE Robotics and Automation Letters, 2(2), 765–772. http://doi.org/10.1109/LRA.2017.2651952

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  • Dexterity Analysis of Three 6-DOF Continuum Robots Combining Concentric Tube Mechanisms and Cable-Driven Mechanisms

    Wu, L., Crawford, R., & Roberts, J. (2017). Dexterity Analysis of Three 6-DOF Continuum Robots Combining Concentric Tube Mechanisms and Cable-Driven Mechanisms. IEEE Robotics and Automation Letters, 2(2), 514–521. http://doi.org/10.1109/LRA.2016.2645519

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  • Orthopaedic surgeon attitudes towards current limitations and the potential for robotic and technological innovation in arthroscopic surgery

    Jaiprakash, A., O’Callaghan, W. B., Whitehouse, S. L., Pandey, A., Wu, L., Roberts, J., & Crawford, R. W. (2017). Orthopaedic surgeon attitudes towards current limitations and the potential for robotic and technological innovation in arthroscopic surgery. Journal of Orthopaedic Surgery, 25(1), 230949901668499. http://doi.org/10.1177/2309499016684993

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  • Farm Workers of the Future: Vision-Based Robotics for Broad-Acre Agriculture

    Ball, D., Ross, P., English, A., Milani, P., Richards, D., Bate, A., Upcroft, B., Wyeth, G., Corke, P. (2017). Farm Workers of the Future: Vision-Based Robotics for Broad-Acre Agriculture. IEEE Robotics & Automation Magazine, 1–1. http://doi.org/10.1109/MRA.2016.2616541

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  • Image-Based Visual Servoing With Unknown Point Feature Correspondence

    McFadyen, A., Jabeur, M., & Corke, P. (2017). Image-Based Visual Servoing With Unknown Point Feature Correspondence. IEEE Robotics and Automation Letters, 2(2), 601–607. http://doi.org/10.1109/LRA.2016.2645886

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  • Image-Based Visual Servoing With Light Field Cameras

    Tsai, D., Dansereau, D. G., Peynot, T., & Corke, P. (2017). Image-Based Visual Servoing With Light Field Cameras. IEEE Robotics and Automation Letters, 2(2), 912–919. http://doi.org/10.1109/LRA.2017.2654544

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  • Spherepix: A Data Structure for Spherical Image Processing

    Adarve, J. D., & Mahony, R. (2017). Spherepix: A Data Structure for Spherical Image Processing. IEEE Robotics and Automation Letters, 2(2), 483–490. http://doi.org/10.1109/LRA.2016.2645119

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  • Motion Segmentation Via a Sparsity Constraint

    Lai, T., Wang, H., Yan, Y., Chin, T.-J., & Zhao, W.-L. (2016). Motion Segmentation Via a Sparsity Constraint. IEEE Transactions on Intelligent Transportation Systems, PP(99), 1–11. http://doi.org/10.1109/TITS.2016.2596296 *Article in press

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  • Clustering with Hypergraphs: The Case for Large Hyperedges

    Purkait, P., Chin, T. J., Sadri, A., & Suter, D. (2017). Clustering with Hypergraphs: The Case for Large Hyperedges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(9), 1697–1711. https://doi.org/10.1109/TPAMI.2016.2614980

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Conference Papers

  • Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor

    Stoffregen, T., & Kleeman, L. (2017). Simultaneous optical flow and segmentation (SOFAS) using dynamic vision sensor. Australasian Conference on Robotics and Automation, ACRA, 2017-December, 52–61.

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  • Towards Context-Aware Interaction Recognition for Visual Relationship Detection

    Zhuang, B., Liu, L., Shen, C., & Reid, I. (2017). Towards Context-Aware Interaction Recognition for Visual Relationship Detection. Proceedings of the IEEE International Conference on Computer Vision, 2017-October, 589–598. https://doi.org/10.1109/ICCV.2017.71

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  • Semi-dense visual odometry for RGB-D cameras using approximate nearest neighbour fields

    Zhou, Y., Kneip, L., & Li, H. (2017). Semi-dense visual odometry for RGB-D cameras using approximate nearest neighbour fields. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6261–6268). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989742

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  • Accurate extrinsic calibration between monocular camera and sparse 3D Lidar points without markers

    Xiao, Z., Li, H., Zhou, D., Dai, Y., & Dai, B. (2017). Accurate extrinsic calibration between monocular camera and sparse 3D Lidar points without markers. In 2017 IEEE Intelligent Vehicles Symposium (IV) (pp. 424–429). Los Angeles, CA, USA: IEEE. http://doi.org/10.1109/IVS.2017.7995755

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  • Multi-attention Network for One Shot Learning

    Wang, P., Liu, L., Shen, C., Huang, Z., Hengel, A. van den, & Shen, H. T. (2017). Multi-attention Network for One Shot Learning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6212–6220). Honolulu, HI, USA: IEEE. http://doi.org/10.1109/CVPR.2017.658

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  • Joint pose and principal curvature refinement using quadrics

    Spek, A., & Drummond, T. (2017). Joint pose and principal curvature refinement using quadrics. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3968–3975). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989456

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  • CREST: Convolutional Residual Learning for Visual Tracking

    Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R. W. H., & Yang, M.-H. (2017). CREST: Convolutional Residual Learning for Visual Tracking. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2574–2583). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.279

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  • Encouraging LSTMs to Anticipate Actions Very Early

    Aliakbarian, M. S., Saleh, F. S., Salzmann, M., Fernando, B., Petersson, L., & Andersson, L. (2017). Encouraging LSTMs to Anticipate Actions Very Early. Proceedings of the IEEE International Conference on Computer Vision, 2017-October, 280–289. https://doi.org/10.1109/ICCV.2017.39

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  • CNN-based small object detection and visualization with feature activation mapping

    Menikdiwela, M., Nguyen, C., Li, H., & Shaw, M. (2018). CNN-based small object detection and visualization with feature activation mapping. International Conference Image and Vision Computing New Zealand, 2017-December, 1–5. https://doi.org/10.1109/IVCNZ.2017.8402455

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  • Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior

    Maicas, G., Carneiro, G., & Bradley, A. P. (2017). Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (pp. 305–309). Melbourne, Australia: IEEE. http://doi.org/10.1109/ISBI.2017.7950525

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  • When Unsupervised Domain Adaptation Meets Tensor Representations

    Lu, H., Zhang, L., Cao, Z., Wei, W., Xian, K., Shen, C., & Hengel, A. van den. (2017). When Unsupervised Domain Adaptation Meets Tensor Representations. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 599–608). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.72

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  • Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map

    Liu, L., Li, H., & Dai, Y. (2017). Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2391–2400). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.260

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  • Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks

    Li, H., Wang, P., & Shen, C. (2017). Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 5248–5256). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.560

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  • RATSAC – Random Tree Sampling for Maximum Consensus Estimation

    Le, H., Chin, T.-J., & Suter, D. (2017). RATSAC - Random Tree Sampling for Maximum Consensus Estimation. In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–8). Sydney, Australia: IEEE. http://doi.org/10.1109/DICTA.2017.8227480

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  • An Exact Penalty Method for Locally Convergent Maximum Consensus

    Le, H., Chin, T.-J., & Suter, D. (2017). An Exact Penalty Method for Locally Convergent Maximum Consensus. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 379–387). Honolulu, USA: IEEE. http://doi.org/10.1109/CVPR.2017.48

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  • Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames

    Kumar, S., Dai, Y., & Li, H. (2017). Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 4659–4667). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.498

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  • RRD-SLAM: Radial-distorted rolling-shutter direct SLAM

    Kim, J.-H., Latif, Y., & Reid, I. (2017). RRD-SLAM: Radial-distorted rolling-shutter direct SLAM. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5148–5154). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989602

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  • A discrete-time attitude observer on SO(3) for vision and GPS fusion

    Khosravian, A., Chin, T.-J., Reid, I., & Mahony, R. (2017). A discrete-time attitude observer on SO(3) for vision and GPS fusion. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5688–5695). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989669

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  • Scaling CNNs for High Resolution Volumetric Reconstruction From a Single Image

    Johnston, A., Garg, R., Carneiro, G., Reid, I., & Van Den Hengel, A. (2017). Scaling CNNs for High Resolution Volumetric Reconstruction from a Single Image. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 2018-January, 930–939. https://doi.org/10.1109/ICCVW.2017.114

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  • Deep Subspace Clustering Networks

    Ji, P., Zhang, T., Li, H., & Salzmann EPFL -CVLab Ian Reid, M. (2017). Deep Subspace Clustering Networks. In 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach, CA, USA. Retrieved from http://papers.nips.cc/paper/6608-deep-subspace-clustering-networks.pdf

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  • “Maximizing Rigidity” Revisited: A Convex Programming Approach for Generic 3D Shape Reconstruction from Multiple Perspective Views

    Ji, P., Li, H., Dai, Y., & Reid, I. (2017). “Maximizing Rigidity” Revisited: A Convex Programming Approach for Generic 3D Shape Reconstruction from Multiple Perspective Views. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 929–937). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.106

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  • Fast Incremental Bundle Adjustment with Covariance Recovery

    Ila, V., Polok, L., Solony, M., & Istenic, K. (2018). Fast incremental bundle adjustment with covariance recovery. Proceedings - 2017 International Conference on 3D Vision, 3DV 2017, 175–184. https://doi.org/10.1109/3DV.2017.00029

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  • Point and line feature-based observer design on SL(3) for Homography estimation and its application to image stabilization

    Hua, M.-D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2017). Point and line feature-based observer design on SL(3) for Homography estimation and its application to image stabilization. In International Conference on Robotics and Automation (ICRA). Singapore. Retrieved from https://hal.archives-ouvertes.fr/hal-01628175/

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  • Explicit Complementary Observer Design on Special Linear Group SL(3) for Homography Estimation using Conic Correspondences

    Hua, M. D., Hamel, T., Mahony, R., & Allibert, G. (2018). Explicit complementary observer design on Special Linear Group SL(3) for homography estimation using conic correspondences. 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, 2018-January, 2434–2441. https://doi.org/10.1109/CDC.2017.8264006

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  • Exploring The Effect of Meta-Structural Information on the Global Consistency of SLAM

    Henein, M., Abello, M., Ila, V., & Mahony, R. (2017). Exploring the effect of meta-structural information on the global consistency of SLAM. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 1616–1623. https://doi.org/10.1109/IROS.2017.8205970

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  • Smart Mining for Deep Metric Learning

    Harwood, B., G, V. K. B., Carneiro, G., Reid, I., & Drummond, T. (2017). Smart Mining for Deep Metric Learning. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2840–2848). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.307

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  • Self-Paced Kernel Estimation for Robust Blind Image Deblurring

    Gong, D., Tan, M., Zhang, Y., Hengel, A. van den, & Shi, Q. (2017). Self-Paced Kernel Estimation for Robust Blind Image Deblurring. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 1670–1679). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.184

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  • Pose Changes From a Different Point of View

    Chirikjian, G. S., Mahony, R., Ruan, S., & Trumpf, J. (2017). Pose Changes From a Different Point of View. In ASME 2017 41st Mechanisms and Robotics Conference. Cleveland, Ohio: ASME. http://doi.org/10.1115/DETC2017-67725

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  • Generalized Rank Pooling for Activity Recognition

    Cherian, A., Fernando, B., Harandi, M., & Gould, S. (2017). Generalized Rank Pooling for Activity Recognition. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1581–1590). Honolulu, HI, USA: IEEE. http://doi.org/10.1109/CVPR.2017.172

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  • Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence

    Campbell, D., Petersson, L., Kneip, L., & Li, H. (2017). Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 1–10). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.10

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  • Enhancing Feature Discrimination for Unsupervised Hashing

    Hoang, T., Do, T. T., Le Tan, D. K., & Cheung, N. M. (2018). Enhancing feature discrimination for unsupervised hashing. Proceedings - International Conference on Image Processing, ICIP, 2017-September, 3710–3714. https://doi.org/10.1109/ICIP.2017.8296975

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  • Learning Camera Pose from Optical Colonoscopy Frames Through Deep Convolutional Neural Network (CNN)

    Armin M.A., Barnes N., Alvarez J., Li H., Grimpen F., Salvado O. (2017) Learning Camera Pose from Optical Colonoscopy Frames Through Deep Convolutional Neural Network (CNN). In: Cardoso M. et al. (eds) Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures. CARE 2017, CLIP 2017. Lecture Notes in Computer Science, vol 10550. Springer, Cham. https://doi.org/10.1007/978-3-319-67543-5_5

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  • Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination

    Zhang, F., Leitner, J., Milford, M., & Corke, P. I. (2017). Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017-July, 496–497. https://doi.org/10.1109/CVPRW.2017.74

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  • Action recognition: From static datasets to moving robots

    Rezazadegan, F., Shirazi, S., Upcrofit, B., & Milford, M. (2017). Action recognition: From static datasets to moving robots. Proceedings - IEEE International Conference on Robotics and Automation, 3185–3191. https://doi.org/10.1109/ICRA.2017.7989361

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  • 3D tracking of water hazards with polarized stereo cameras

    Nguyen, C. V., Milford, M., & Mahony, R. (2017). 3D tracking of water hazards with polarized stereo cameras. Proceedings - IEEE International Conference on Robotics and Automation, 5251–5257. https://doi.org/10.1109/ICRA.2017.7989616

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  • Towards Unsupervised Weed Scouting for Agricultural Robotics

    Hall, D., Dayoub, F., Kulk, J., & McCool, C. (2017). Towards unsupervised weed scouting for agricultural robotics. Proceedings - IEEE International Conference on Robotics and Automation, 5223–5230. https://doi.org/10.1109/ICRA.2017.7989612

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  • Improving condition- and environment-invariant place recognition with semantic place categorization

    Garg, S., Jacobson, A., Kumar, S., & Milford, M. (2017). Improving condition- and environment-invariant place recognition with semantic place categorization. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 6863–6870. https://doi.org/10.1109/IROS.2017.8206608

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  • Episode-Based Active Learning with Bayesian Neural Networks

    Dayoub, F., Sunderhauf, N., & Corke, P. I. (2017). Episode-Based Active Learning with Bayesian Neural Networks. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017-July, 498–500. https://doi.org/10.1109/CVPRW.2017.75

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  • A behaviour tree-based robust decision framework for enhanced UAV autonomy

    Crofts, D., Bruggemann, T. S., & Ford, J. J. (2017). A behaviour tree-based robust decision framework for enhanced UAV autonomy. In 17th Australian International Aerospace Congress (AIAC17). Melbourne, Victoria. Retrieved from http://eprints.qut.edu.au/106017/

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  • Deep learning features at scale for visual place recognition

    Chen, Z., Jacobson, A., Sunderhauf, N., Upcroft, B., Liu, L., Shen, C., Reid, I., & Milford, M. (2017). Deep learning features at scale for visual place recognition. Proceedings - IEEE International Conference on Robotics and Automation, 3223–3230. https://doi.org/10.1109/ICRA.2017.7989366

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  • A Transplantable System for Weed Classification by Agricultural Robotics

    Hall, D., Dayoub, F., Perez, T., & McCool, C. (2017). A transplantable system for weed classification by agricultural robotics. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 5174–5179. https://doi.org/10.1109/IROS.2017.8206406

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  • Meaningful maps with object-oriented semantic mapping

    Sunderhauf, N., Pham, T. T., Latif, Y., Milford, M., & Reid, I. (2017). Meaningful maps with object-oriented semantic mapping. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 5079–5085. https://doi.org/10.1109/IROS.2017.8206392

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  • Unsupervised Human Action Detection by Action Matching

    Fernando, B., Shirazi, S., & Gould, S. (2017). Unsupervised Human Action Detection by Action Matching. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017-July, 1604–1612. https://doi.org/10.1109/CVPRW.2017.205

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  • Neural Aggregation Network for Video Face Recognition

    Yang, J., Ren, P., Zhang, D., Chen, D., Wen, F., Li, H., & Hua, G. (2017). Neural aggregation network for video face recognition. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 5216–5225. https://doi.org/10.1109/CVPR.2017.554

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  • Guided Open Vocabulary Image Captioning with Constrained Beam Search.

    Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2017). Guided Open Vocabulary Image Captioning with Constrained Beam Search. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 936–945. https://doi.org/10.18653/v1/D17-1098

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  • The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions

    Wang, P., Wu, Q., Shen, C., & Van Den Hengel, A. (2017). The VQA-machine: Learning how to use existing vision algorithms to answer new questions. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 3909–3918. https://doi.org/10.1109/CVPR.2017.416

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  • Sequential Person Recognition in Photo Albums with a Recurrent Network

    Li, Y., Lin, G., Zhuang, B., Liu, L., Shen, C., & Van Den Hengel, A. (2017). Sequential person recognition in photo albums with a recurrent network. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 5660–5668. https://doi.org/10.1109/CVPR.2017.600

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  • RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

    Lin, G., Milan, A., Shen, C., & Reid, I. (2017). RefineNet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 5168–5177. https://doi.org/10.1109/CVPR.2017.549

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  • Infinite Variational Autoencoder for Semi-Supervised Learning

    Abbasnejad, M. E., Dick, A., & Van Den Hengel, A. (2017). Infinite variational autoencoder for semi-supervised learning. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 781–790. https://doi.org/10.1109/CVPR.2017.90

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  • Graph-Structured Representations for Visual Question Answering

    Teney, D., Liu, L., & Van Den Hengel, A. (2017). Graph-structured representations for visual question answering. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 3233–3241. https://doi.org/10.1109/CVPR.2017.344

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  • DeepSetNet: Predicting Sets with Deep Neural Networks

    Rezatofighi, S. H., Vijay Kumar, B. G., Milan, A., Abbasnejad, E., Dick, A., & Reid, I. (2017). DeepSetNet: Predicting Sets with Deep Neural Networks. Proceedings of the IEEE International Conference on Computer Vision, 2017-October, 5257–5266. https://doi.org/10.1109/ICCV.2017.561

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  • Attend in groups: a weakly-supervised deep learning framework for learning from web data

    Zhuang, B., Liu, L., Li, Y., Shen, C., & Reid, I. (2017). Attend in groups: A weakly-supervised deep learning framework for learning from web data. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 2915–2924. https://doi.org/10.1109/CVPR.2017.311

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  • FPGA acceleration of multilevel ORB feature extraction for computer vision

    Weberruss, J., Kleeman, L., Boland, D., & Drummond, T. (2017). FPGA acceleration of multilevel ORB feature extraction for computer vision. In 2017 27th International Conference on Field Programmable Logic and Applications (FPL) (pp. 1–8). Ghent, Belgium: IEEE. http://doi.org/10.23919/FPL.2017.8056856

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  • Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation

    Zuo, Y., & Drummond, T. (2017). Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation. In Proceedings of the 1st Annual Conference on Robot Learning, in PMLR 78 (pp. 27–36). Retrieved from http://proceedings.mlr.press/v78/zuo17a.html

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  • A compact parametric solution to depth sensor calibration

    Spek, A., Drummond, T. (2017) A compact parametric solution to depth sensor calibration. In 28th British Machine Vision Conference (BMVC). London: https://bmvc2017.london/proceedings/

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  • Joint Dimensionality Reduction and Metric Learning: A Geometric Take

    Harandi, M., Salzmann, M., & Hartley, R. (2017). Joint Dimensionality Reduction and Metric Learning: A Geometric Take. In Proceedings of the 34th International Conference on Machine Learning (ICML). Sydney, Australia.

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  • Quickest detection of intermittent signals with estimated anomaly times

    James, J., Ford, J. J., & Molloy, T. L. (2018). Quickest detection of intermittent signals with estimated anomaly times. 2017 Asian Control Conference, ASCC 2017, 2018-January, 2066–2070. https://doi.org/10.1109/ASCC.2017.8287493

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  • Going deeper: Autonomous steering with neural memory networks. In IEEE Conference on Computer Vision and Pattern Recognition

    Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2017). Going deeper: Autonomous steering with neural memory networks. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 2018-January, 214–221. https://doi.org/10.1109/ICCVW.2017.34

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  • Combining Line Segments and Points for Appearance- based Indoor Navigation by Image Based Visual Servoing

    Bista, S. R., Giordano, P. R., & Chaumette, F. (2017). Combining line segments and points for appearance-based indoor navigation by image based visual servoing. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 2960–2967. https://doi.org/10.1109/IROS.2017.8206131

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  • The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research

    Leitner, J., Tow, A. W., Sunderhauf, N., Dean, J. E., Durham, J. W., Cooper, M., … Corke, P. (2017). The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 4705–4712). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989545

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  • Improved Semantic segmentation for robotic applications with hierarchical conditional random fields

    Meyer, B. J., & Drummond, T. (2017). Improved semantic segmentation for robotic applications with hierarchical conditional random fields. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5258–5265). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989617

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  • Solving Robust Regularization Problems Using Iteratively Re-weighted Least Squares

    Kiani, K. A., & Drummond, T. (2017). Solving Robust Regularization Problems Using Iteratively Re-weighted Least Squares. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 483–492). Santa Rosa, CA: IEEE. http://doi.org/10.1109/WACV.2017.60

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  • Higher-Order Pooling of CNN Features via Kernel Linearization for Action Recognition

    Cherian, A., Koniusz, P., & Gould, S. (2017). Higher-Order Pooling of CNN Features via Kernel Linearization for Action Recognition. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 130–138). Santa Rosa, CA: IEEE. http://doi.org/10.1109/WACV.2017.22

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  • Ordered Pooling of Optical Flow Sequences for Action Recognition

    Wang, J., Cherian, A., & Porikli, F. (2017). Ordered Pooling of Optical Flow Sequences for Action Recognition. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 168–176). Santa Rosa, CA: IEEE. http://doi.org/10.1109/WACV.2017.26

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  • Dense monocular reconstruction using surface normals

    Weerasekera, C. S., Latif, Y., Garg, R., & Reid, I. (2017). Dense monocular reconstruction using surface normals. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2524–2531). IEEE. https://doi.org/10.1109/ICRA.2017.7989293

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  • Standard operating procedures for UAV or drone basedmonitoring of wildlife

    Gonzalez, F., & Johnson, S. (2017). Standard operating procedures for UAV or drone basedmonitoring of wildlife. In Proceedings of Unmanned Aircraft Systems for Remote Sensing) UAS4RS 2017. Hobart, Tasmania. Retrieved from https://eprints.qut.edu.au/108859/

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  • Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs

    Dharmasiri, T., Spek, A., & Drummond, T. (2017). Joint prediction of depths, normals and surface curvature from RGB images using CNNs. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1505–1512). Vancouver, Canada: IEEE. http://doi.org/10.1109/IROS.2017.8205954

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  • A vision-based sense-and-avoid system tested on a ScanEagle UAV

    Bratanov, D., Mejias, L., & Ford, J. J. (2017). A vision-based sense-and-avoid system tested on a ScanEagle UAV. 2017 International Conference on Unmanned Aircraft Systems, ICUAS 2017, 1134–1142. https://doi.org/10.1109/ICUAS.2017.7991302

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  • Self-Supervised Video Representation Learning With Odd-One-Out Networks

    Fernando, B., Bilen, H., Gavves, E., & Gould, S. (2017). Self-supervised video representation learning with odd-one-out networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 5729–5738. https://doi.org/10.1109/CVPR.2017.607

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Edited Collection

  • Modular Design of Image Based Visual Servo Control for Dynamic Mechanical Systems

    Mahony, R. (2017). Modular Design of Image Based Visual Servo Control for Dynamic Mechanical Systems. In Robotics Research (pp. 129–146). Springer International Publishing. http://doi.org/10.1007/978-3-319-29363-9_8

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  • Introduction to the special section on Artificial Intelligence and Computer Vision

    Lu, H., Guna, J., & Dansereau, D. G. (2017). Introduction to the special section on Artificial Intelligence and Computer Vision. Computers & Electrical Engineering, 58, 444–446. http://doi.org/10.1016/j.compeleceng.2017.04.024

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