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Publications

2016 All Categories [217]

Computer Vision and Image Understanding (Vol. 146)

Reid, I. (2016). 12th Asian conference on computer vision. Computer Vision and Image Understanding (Vol. 146).

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Robotics Research: The 16th International Symposium ISRR

Inaba, M., & Corke, P. (2016). Robotics research: The 16th international symposium ISRR. In 16th International Symposium of Robotics Research, ISRR 2013 (Vol. 114). Singapore: Springer Verlag.

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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. (2016). Attend in groups: a weakly-supervised deep learning framework for learning from web data. Retrieved from https://arxiv.org/abs/1611.09960

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Not All Negatives are Equal: Learning to Track with Multiple Background Clusters

*Zhu, G., Porikli, F., & Li, H. (2016). Not All Negatives are Equal: Learning to Track with Multiple Background Clusters. IEEE Transactions on Circuits and Systems for Video Technology, PP (99), 1–1. Retrieved from http://doi.org/10.1109/TCSVT.2016.2615518

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Neural Aggregation Network for Video Face Recognition

*Yang, J., Ren, P., Chen, D., Wen, F., Li, H., & Hua, G. (2016). Neural Aggregation Network for Video Face Recognition. Retrieved from http://arxiv.org/abs/1603.05474

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Automatic Event Detection for Signal-based Surveillance

Xu, J., Fookes, C., & Sridharan, S. (2016). Automatic Event Detection for Signal-based Surveillance. Retrieved from http://arxiv.org/abs/1612.01611

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Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

*Wu, Z., Shen, C., & Hengel, A. van den. (2016). Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Retrieved from http://arxiv.org/abs/1611.10080

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Image Captioning and Visual Question Answering Based on Attributes and External Knowledge

*Wu, Q., Shen, C., Hengel, A. van den, Wang, P., & Dick, A. (2016). Image Captioning and Visual Question Answering Based on Attributes and External Knowledge. Retrieved from http://arxiv.org/abs/1603.02814

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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. (n.d.). The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions. Retrieved from https://arxiv.org/abs/1612.05386

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Hi Detector, What’s Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution

*Wang, P., Liu, L., Shen, C., Hengel, A. van den, & Shen, H. T. (2016). Hi Detector, What’s Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution. Retrieved from http://arxiv.org/abs/1602.04422

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Mirrored Light Field Video Camera Adapter

*Tsai, D., Dansereau, D. G., Martin, S., & Corke, P. (2016). Mirrored Light Field Video Camera Adapter. Retrieved from http://arxiv.org/abs/1612.05335

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Graph-Structured Representations for Visual Question Answering

*Teney, D., Liu, L., & Hengel, A. van den. (2016). Graph-Structured Representations for Visual Question Answering. Retrieved from http://arxiv.org/abs/1609.05600

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Meaningful Maps $-$ Object-Oriented Semantic Mapping

*Sünderhauf, N., Pham, T. T., Latif, Y., Milford, M., & Reid, I. (2016). Meaningful Maps $-$ Object-Oriented Semantic Mapping. Retrieved from http://arxiv.org/abs/1609.07849

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DeepSetNet: Predicting Sets with Deep Neural Networks

*Rezatofighi, S. H., G, V. K. B., Milan, A., Abbasnejad, E., Dick, A., & Reid, I. (2016). DeepSetNet: Predicting Sets with Deep Neural Networks. Retrieved from http://arxiv.org/abs/1611.08998

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Clustering with Hypergraphs: The Case for Large Hyperedges

*Purkait, P., Chin, T.-J., Sadri, A., & Suter, D. (2016). Clustering with Hypergraphs: The Case for Large Hyperedges. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP (99), 1–1. Retrieved from http://doi.org/10.1109/TPAMI.2016.2614980

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MOT16: A Benchmark for Multi-Object Tracking. Retrieved

*Milan, A., Leal-Taixe, L., Reid, I., Roth, S., & Schindler, K. (2016). MOT16: A Benchmark for Multi-Object Tracking. Retrieved from http://arxiv.org/abs/1603.00831

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Robust and Efficient Relative Pose with a Multi-camera System for Autonomous Vehicle in Highly Dynamic Environments

*Liu, L., Li, H., & Dai, Y. (2016). Robust and Efficient Relative Pose with a Multi-camera System for Autonomous Vehicle in Highly Dynamic Environments. Retrieved from http://arxiv.org/abs/1605.03689

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Exploring Context with Deep Structured models for Semantic Segmentation

*Lin, G., Shen, C., Hengel, A. van den, & Reid, I. (2016). Exploring Context with Deep Structured models for Semantic Segmentation. Retrieved from http://arxiv.org/abs/1603.03183

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

*Lin, G., Milan, A., Shen, C., & Reid, I. (2016). RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation. Retrieved from http://arxiv.org/abs/1611.06612

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Sequential Person Recognition in Photo Albums with a Recurrent Network

*Li, Y., Lin, G., Zhuang, B., Liu, L., Shen, C., & Hengel, A. van den. (2016). Sequential Person Recognition in Photo Albums with a Recurrent Network. Retrieved from http://arxiv.org/abs/1611.09967

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The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research

*Leitner, J., Tow, A. W., Dean, J. E., Suenderhauf, N., Durham, J. W., Cooper, M., et al. (2016). The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research. Retrieved from http://arxiv.org/abs/1609.05258

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Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods

Harandi, M., Salzmann, M., & Hartley, R. (2016). Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods. Retrieved from http://arxiv.org/abs/1605.06182

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Generalized BackPropagation Etude De Cas: Orthogonality

*Harandi, M., & Fernando, B. (2016). Generalized BackPropagation, ’{E}tude De Cas: Orthogonality. Retrieved from http://arxiv.org/abs/1611.05927

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On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization

Gould, S., Fernando, B., Cherian, A., Anderson, P., Cruz, R. S., & Guo, E. (2016). On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization. Retrieved from http://arxiv.org/abs/1607.05447

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Unsupervised Human Action Detection by Action Matching

*Fernando, B., Shirazi, S., & Gould, S. (2016). Unsupervised Human Action Detection by Action Matching. Retrieved from http://arxiv.org/abs/1612.00558

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Rank Pooling for Action Recognition

*Fernando, B., Gavves, E., Oramas, J., Ghodrati, A., & Tuytelaars, T. (2016). Rank Pooling for Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP (99), 1–1. Retrieved from http://doi.org/10.1109/TPAMI.2016.2558148

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

*Fernando, B., Bilen, H., Gavves, E., & Gould, S. (2016). Self-Supervised Video Representation Learning With Odd-One-Out Networks. Retrieved from http://arxiv.org/abs/1611.06646

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ARTiS: Appearance-based Action Recognition in Task Space

* Eich, M., Shirazi, S., & Wyeth, G. (2016). ARTiS: Appearance-based Action Recognition in Task Space. Retrieved from https://arxiv.org/abs/1610.05432

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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. (2016). Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters. Computer Vision and Image Understanding. Retrieved from http://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. (2016). Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation. Retrieved from http://arxiv.org/abs/1610.01706

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Advances in Visual Computing

* Bebis, G., Boyle, R., Parvin, et al. (2016). Advances in Visual Computing. (Vol. 10072). Cham: Springer International Publishing. Retrieved from http://doi.org/10.1007/978-3-319-50835-1

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Aerodynamics of Rotor Blades for Quadrotors

Bangura, M., Melega, M., Naldi, R., & Mahony, R. (2016). Aerodynamics of Rotor Blades for Quadrotors. Retrieved from http://arxiv.org/abs/1601.00733

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Guided Open Vocabulary Image Captioning with Constrained Beam Search

*Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016). Guided Open Vocabulary Image Captioning with Constrained Beam Search. Retrieved from http://arxiv.org/abs/1612.00576

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Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation

*Aliakbarian, M. S., Saleh, F., Fernando, B., Salzmann, M., Petersson, L., & Andersson, L. (2016). Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation. Retrieved from http://arxiv.org/abs/1611.05520

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Spherepix: a Data Structure for Spherical Image Processing

*Adarve, J. D., & Mahony, R. (2016). Spherepix: a Data Structure for Spherical Image Processing. IEEE Robotics and Automation Letters, PP (99), 1–1. Retrieved from http://doi.org/10.1109/LRA.2016.2645119

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Infinite Variational Autoencoder for Semi-Supervised Learning

*Abbasnejad, E., Dick, A., & Hengel, A. van den. (2016). Infinite Variational Autoencoder for Semi-Supervised Learning. Retrieved from http://arxiv.org/abs/1611.07800

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Fast Training of Triplet-based Deep Binary Embedding Networks

*Zhuang, B., Lin, G., Shen, C., & Reid, I. (2016). Fast Training of Triplet-based Deep Binary Embedding Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016. Las Vegas, Nevada.

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Robust Visual Tracking with Deep Convolutional Neural Network Based Object Proposals on PETS

*Zhu, G., Porikli, F., & Li, H. (2016). Robust Visual Tracking with Deep Convolutional Neural Network Based Object Proposals on PETS. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 (pp. 1265–1272). Las Vegas, Nevada: IEEE Computer Society.

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Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals

*Zhu, G., Porikli, F., & Li, H. (2016). Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 943–951). Las Vegas, Nevada.

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Real-time Rotation Estimation for Dense Depth Sensors in Piece-wise Planar Environments

*Zhou, Y., Kneip, L., & Li, H. (2016). Real-time Rotation Estimation for Dense Depth Sensors in Piece-wise Planar Environments. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Daejeon, Korea.

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A Revisit of Methods for Determining the Fundamental Matrix with Planes

*Zhou, Y., Kneip, L., & Li, H. (2015). A Revisit of Methods for Determining the Fundamental Matrix with Planes. In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE

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Less Is More: Towards Compact CNNs

*Zhou, H., Alvarez, J. M., & Porikli, F. (2016). Less Is More: Towards Compact CNNs. In Computer Vision – ECCV 2016 (pp. 662–677). Springer, Cham.

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Cluster Sparsity Field for Hyperspectral Imagery Denoising

*Zhang, L., Wei, W., Zhang, Y., Shen, C., van den Hengel, A., & Shi, Q. (2016). Cluster Sparsity Field for Hyperspectral Imagery Denoising. In Computer Vision - ECCV 2016 (pp. 631–647). Springer International Publishing.

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SLNSW-UTS: A Historical Image Dataset for Image Multi-Labeling and Retrieval

Zhang, J., Zhang, J., Lu, J., Shen, C., Curr, K., Phua, R., et al. (2016). SLNSW-UTS: A Historical Image Dataset for Image Multi-Labeling and Retrieval. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–6). Gold Coast, Australia: IEEE.

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Vertical Axis Detection for Sport Video Analytics

Zeng, R., Lakemond, R., Denman, S., Sridharan, S., Fookes, C., & Morgan, S. (2016). Vertical Axis Detection for Sport Video Analytics. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE.

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Ultra-Resolving Face Images by Discriminative Generative Networks

*Yu, X., & Porikli, F. (2016). Ultra-Resolving Face Images by Discriminative Generative Networks. In Computer Vision – ECCV 2016 (pp. 318–333). Springer.

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Riemannian Sparse Coding for Classification of PolSAR Images

*Yang, W., Zhong, N., Yang, X., & Cherian, A. (2016). Riemannian sparse coding for classification of PolSAR images. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5698–5701). Beijing, China: Institute of Electrical and Electronics Engineers Inc.

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Robust Optical Flow Estimation of Double-Layer Images under Transparency or Reflection

*Yang, J., Li, H., Dai, Y., & Tan, R. T. (2016). Robust optical flow estimation of double-layer images under transparency or reflection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 1410–1419). Las Vegas, Nevada: IEEE Computer Society.

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Superpixel-Based Two-View Deterministic Fitting for Multiple-Structure Data

Xiao, G., Wang, H., Yan, Y., & Suter, D. (2016). Superpixel-Based Two-View Deterministic Fitting for Multiple-Structure Data. In Computer Vision – ECCV 2016 (pp. 517–533). Springer, Cham.

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Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources

*Wu, Q., Wang, P., Shen, C., Dick, A., & Van Den Hengel, A. (2016). Ask me anything: Free-form visual question answering based on knowledge from external sources. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 4622–4630). Las Vegas, Nevada: IEEE Computer Society.

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What Value Do Explicit High Level Concepts Have in Vision to Language Problems?

*Wu, Q., Shen, C., Liu, L., Dick, A., & Van Den Hengel, A. (2016). What value do explicit high level concepts have in vision to language problems? In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 203–212). Las Vegas, Nevada: IEEE Computer Society.

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Towards Hybrid Control of a Flexible Curvilinear Surgical Robot With Visual/Haptic Guidance

Wu, L., Wu, K., & Ren, H. (2016). Towards hybrid control of a flexible curvilinear surgical robot with visual/haptic guidance. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 (pp. 501–507). Daejeon, Korea: Institute of Electrical and Electronics Engineers Inc.

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Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering

*Wang, Y., Wenjie, Z., Wu, L., Lin, X., Fang, M., & Pan, S. (2016). Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2016–January, pp. 2153–2159). New York, United States.

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Collaborative Multi-Sensor Image Transmission and Data Fusion in Mobile Visual Sensor Networks Equipped with RGB-D Cameras

*Wang, X., Sekercioglu, A., Drummond, T., Natalizio, E., Fantoni, I., & Fremont, V. (2016). Collaborative Multi-Sensor Image Transmission and Data Fusion in Mobile Visual Sensor Networks Equipped with RGB-D Cameras. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016). Baden-Baden, Germany.

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UAV Based Target Finding and Tracking in GPS-Denied and Cluttered Environments

*Vanegas, F., Campbell, D., Eich, M., & Gonzalez, F. (2016). UAV based target finding and tracking in GPS-denied and cluttered environments. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 (pp. 2307–2313). Daejeon, South Korea: Institute of Electrical and Electronics Engineers Inc.

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Find my office: Navigating real space from semantic descriptions

Talbot, B., Lam, O., Schulz, R., Dayoub, F., Upcroft, B., & Wyeth, G. (2016). Find my office: Navigating real space from semantic descriptions. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5782–5787). IEEE.

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Place Categorization and Semantic Mapping on a Mobile Robot

*Sunderhauf, N., Dayoub, F., McMahon, S., Talbot, B., Schulz, R., Corke, P., et.al. (2016). Place categorization and semantic mapping on a mobile robot. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5729–5736). IEEE.

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Learning Functional Argument Mappings for Hierarchical Tasks from Situation Specific Explanations

Suddrey, G., Eich, M., Maire, F., & Roberts, J. (2016). Learning Functional Argument Mappings for Hierarchical Tasks from Situation Specific Explanations. In AI 2016: Advances in Artificial Intelligence (pp. 345–352). Springer, Cham.

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Towards Robotic Arthroscopy: ‘Instrument gap’ Segmentation

*Strydom, M., Jaiprakash, A., Crawford, R., Peynot, T., & Roberts, J. M. (2016). Towards robotic arthroscopy: “Instrument gap” segmentation. In Australasian Conference on Robotics and Automation (ACRA) 2016. Brisbane, Queensland: Australian Robotics & Automation Association.

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Skyline-based Localisation for Aggressively Manoeuvring Robots using UV sensors and Spherical Harmonics

*Stone, T., Differt, D., Milford, M., & Webb, B. (2016). Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5615–5622). Stockholm: IEEE.

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High-Fidelity Simulation for Evaluating Robotic Vision Performance

*Skinner, J., Garg, S., Sunderhauf, N., Corke, P., Upcroft, B., & Milford, M. (2016). High-fidelity simulation for evaluating robotic vision performance. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Daejeon, Korea

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Bags of Affine Subspaces for Robust Object Tracking

*Shirazi, S., Sanderson, C., McCool, C., & Harandi, M. T. (2015). Bags of Affine Subspaces for Robust Object Tracking. In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE.

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Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

*Saleh, F., Ali Akbarian, M. S., Salzmann, M., Petersson, L., Gould, S., & Alvarez, J. M. (2016). Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation. In Computer Vision - ECCV 2016 (pp. 413–432). Springer International Publishing.

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Joint Probabilistic Matching Using m-Best Solutions

*Rezatofighi, S. H., Milan, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2016). Joint probabilistic matching using m-best solutions. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 136–145). Las Vegas, Nevada: IEEE Computer Society.

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Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression

*Qiao, R., Liu, L., Shen, C., & Van Den Hengel, A. (2016). Less is more: Zero-shot learning from online textual documents with noise suppression. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 2249–2257). Las Vegas, Nevada: IEEE Computer Society.

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Design and Fabrication of a Disposable Micro End Effector for Concentric Tube Robots

*Prasai, A. B., Jaiprakash, A., Pandey, A. K., Crawford, R., Roberts, J., & Wu, L. (2016). Design and fabrication of a disposable micro end effector for concentric tube robots Design and Fabrication of a Disposable Micro End Effector for Concentric Tube Robots. In 14th International Conference on Control, Automation, Robotics and Vision (pp. 13–15). Press.

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3D Reconstruction Quality Analysis and Its Acceleration on GPU Clusters

*Polok, L., Ila, V., & Smrz, P. (2016). 3D reconstruction quality analysis and its acceleration on GPU clusters. In European Signal Processing Conference (EUSIPCO) (Vol. 2016–November, pp. 1108–1112). Budapest, Hungary.

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Efficient Point Process Inference for Large-scale Object Detection

*Pham, T. T., Hamid Rezatofighi, S., Reid, I., & Chin, T.-J. (2016). Efficient Point Process Inference for Large-Scale Object Detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 2837–2845). Las Vegas, Nevada.

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Geometrically Consistent Plane Extraction for Dense Indoor 3D Maps Segmentation

*Pham, T. T., Eich, M., Reid, I., & Wyeth, G. (2016). Geometrically Consistent Plane Extraction for Dense Indoor 3D Maps Segmentation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Daejeon, Korea: IEEE.

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Deeper and Wider Fully Convolutional Network Coupled with Conditional Random Fields for Scene Labeling

Nguyen, K., Fookes, C., & Sridharan, S. (2016). Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 1344–1348). IEEE.

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3D Scanning System for Automatic High-Resolution Plant Phenotyping

*Nguyen, C. V., Fripp, J., Lovell, D. R., Furbank, R., Kuffner, P., Daily, H., & Sirault, X. (2016). 3D Scanning System for Automatic High-Resolution Plant Phenotyping. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–8). Gold Coast, Queensland: IEEE.

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Non-Iterative, Fast SE(3) Path Smoothing

*Ng, Y., Jiang, B., Yu, C., & Li, H. (2016). Non-iterative, fast SE(3) path smoothing. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 (pp. 3172–3179). Daejeon, Korea: Institute of Electrical and Electronics Engineers Inc.

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Latent Structural SVM with Marginal Probabilities for Weakly Labeled Structured Learning

*Namin, S. R., Alvarez, J. M., Kneip, L., & Petersson, L. (2016). Latent structural SVM with marginal probabilities for weakly labeled structured learning. In 23rd IEEE International Conference on Image Processing, ICIP 2016 (pp. 3733–3737). Phoenix, United States: IEEE Computer Society.

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2D Visual Place Recognition for Domestic Service Robots at Night

*Mount, J., & Milford, M. (2016). 2D Visual Place Recognition for Domestic Service Robots at Night. In IEEE International Conference on Robotics and Automation (ICRA 2016) (pp. 4822–4829). Stockholm, Sweden.

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Visual Detection of Occluded Crop: for automated harvesting

*McCool, C., Sa, I., Dayoub, F., Lehnert, C., Perez, T., & Upcroft, B. (2016). Visual Detection of Occluded Crop: for automated harvesting. In IEEE International Conference on Robotics and Automation (ICRA 2016). Stockholm, Sweden.

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Underwater Image Descattering and Quality Assessment

*Lu, H., Li, Y., Xu, X., He, L., Li, Y., Dansereau, D., & Serikawa, S. (2016). Underwater image descattering and quality assessment. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 1998–2002). IEEE.

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Learning Image Matching by Simply Watching Video

*Long, G., Kneip, L., Alvarez, J. M., Li, H., Zhang, X., & Yu, Q. (2016). Learning Image Matching by Simply Watching Video. In Computer Vision - ECCV 2016 (pp. 434–450). Springer International Publishing.

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Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

*Lin, G., Shen, C., Hengel, A. van dan, & Reid, I. (2015). Efficient piecewise training of deep structured models for semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 3194–3203). Las Vegas, Nevada.

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Recent Advances in Camera Planning for Large Area Surveillance

Liu, J., Sridharan, S., & Fookes, C. (2016). Recent Advances in Camera Planning for Large Area Surveillance. ACM Computing Surveys, 49(1), 1–37.

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On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units

*Liao, Z., & Carneiro, G. (2016). On the importance of normalisation layers in deep learning with piecewise linear activation units. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1–8). IEEE.

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Design and Flight Testing of a Bio-Inspired Plume Tracking Algorithm for Unmanned Aerial Vehicles

Letheren, B., Montes, G., Villa, T., & Gonzalez, F. (2016). Design and flight testing of a bio-inspired plume tracking algorithm for unmanned aerial vehicles. In 2016 IEEE Aerospace Conference (pp. 1–9). IEEE.

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LunaRoo: Designing a Hopping Lunar Science Payload

*Leitner, J., Chamberlain, W., Dansereau, D. G., Dunbabin, M., Eich, M., Peynot, T., et.al. (2016). LunaRoo: Designing a hopping lunar science payload. In 2016 IEEE Aerospace Conference (pp. 1–12). IEEE.

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Sweet Pepper Pose Detection and Grasping for Automated Crop Harvesting

*Lehnert, C., Sa, I., McCool, C., Upcroft, B., & Perez, T. (2016). Sweet Pepper Pose Detection and Grasping for Automated Crop Harvesting. In IEEE International Conference on Robotics and Automation (ICRA 2016). Stockholm, Sweden.

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Conformal Surface Alignment With Optimal Mobius Search

*Le, H., Chin, T.-J., & Suter, D. (2016). Conformal Surface Alignment With Optimal Mobius Search. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (pp. 2507–2516). Las Vegas, Nevada.

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Multi-body non-rigid structure-from-motion

*Kumar, S., Dai, Y., & Li, H. (2016). Multi-body non-rigid structure-from-motion. In Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016 (pp. 148–156). Stanford, United States: Institute of Electrical and Electronics Engineers Inc.

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Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions

*Kumar, B. G. V., Carneiro, G., & Reid, I. (2015). Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 5385–5394). Las Vegas, Nevada.

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Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons

*Koniusz, P., Cherian, A., & Porikli, F. (2016). Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons. In Computer Vision – ECCV 2016 (pp. 37–53). Springer, Cham.

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Sparse Coding for Third-order Super-symmetric Tensor Descriptors with Application to Texture Recognition

*Koniusz, P., & Cherian, A. (2016). Sparse Coding for Third-order Super-symmetric Tensor Descriptors with Application to Texture Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 5395–5403). Las Vegas, Nevada.

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The Generalized Relative Pose and Scale Problem: View-Graph Fusion via 2D-2D Registration

*Kneip, L., Sweeney, C., & Hartley, R. (2016). The generalized relative pose and scale problem: View-graph fusion via 2D-2D registration. In IEEE Winter Conference on Applications of Computer Vision, WACV 2016. Lake Placid, United States: Institute of Electrical and Electronics Engineers Inc.

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Direct Semi-dense SLAM for Rolling Shutter Cameras

*Kim, J.-H., Cadena, C., & Reid, I. (2016). Direct semi-dense SLAM for rolling shutter cameras. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1308–1315). Stockholm, Sweden: IEEE.

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Deep Convolutional Neural Networks for Human Embryonic Cell Counting

*Khan, A., Gould, S., & Salzmann, M. (2016). Deep Convolutional Neural Networks for Human Embryonic Cell Counting. In Computer Vision - ECCV 2016 Workshops (pp. 339–348). Springer International Publishing.

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Unmanned Aerial Surveillance System for Hazard Collision Avoidance in Autonomous Shipping

Johansen, T. A., & Perez, T. (2016). Unmanned aerial surveillance system for hazard collision avoidance in autonomous shipping. In 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1056–1065). IEEE.

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Robust Multi-body Feature Tracker: A Segmentation-free Approach

*Ji, P., Li, H., Salzmann, M., & Zhong, Y. (2016). Robust multi-body feature tracker: A segmentation-free approach. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 3843–3851). Las Vegas, Nevada: IEEE Computer Society.

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Haptics-Aided Path Planning and Virtual Fixture Based Dynamic Kinesthetic Boundary for Bilateral Teleoperation of VTOL Aerial Robots

Hou, X., Wang, X., & Mahony, R. (2016). Haptics-aided path planning and virtual fixture based dynamic kinesthetic boundary for bilateral teleoperation of VTOL aerial robots. In 2016 35th Chinese Control Conference (CCC) (pp. 4705–4710). IEEE.

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Adaptive Spatial Filtering for off-axis Digital Holographic Microscopy Based on Region-Recognition Approach with Iterative Thresholding

He, X., Nguyen, C. V., Pratap, M., Zheng, Y., Wang, Y., Nisbet, D. R., et.al. (2016). Adaptive spatial filtering for off-axis digital holographic microscopy based on region recognition approach with iterative thresholding. In SPIE BioPhotonics Australasia (Vol. 10013). Adelaide, Australia: SPIE.

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Feature-based Recursive Observer Design for Homography Estimation

*Hua, M.-D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2016). Feature-based Recursive Observer Design for Homography Estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016.

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FANNG: Fast Approximate Nearest Neighbour Graphs

*Harwood, B., & Drummond, T. (2016). FANNG: Fast Approximate Nearest Neighbour Graphs. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 5713–5722). Las Vegas, Nevada.

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Discovery of Facial Motions using Deep Machine Perception

Ghasemi, A., Denman, S., Sridharan, S., & Fookes, C. (2016). Discovery of facial motions using deep machine perception. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1–7). IEEE.

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Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification

*Ge, Z., McCool, C., Sanderson, C., Wang, P., Liu, L., Reid, I., & Corke, P. (2016). Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification. In Digital Image Computing: Techniques and Applications (DICTA). Gold Coast, Queensland.

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Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks

*Ge, Z., Bewley, A., McCool, C., Corke, P., Upcroft, B., & Sanderson, C. (2016). Fine-grained classification via mixture of deep convolutional neural networks. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1–6). IEEE.

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Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

*Garg, R., Vijay Kumar, B. G., Carneiro, G., & Reid, I. (2016). Unsupervised CNN for single view depth estimation: Geometry to the rescue. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9912 LNCS, pp. 740–756). Springer Verlag.

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Automated Plant and Leaf Separation: Application in 3D Meshes of Wheat Plants

*Frolov, K., Fripp, J., Nguyen, C. V., Furbank, R., Bull, G., Kuffner, P., et.al. (2016). Automated Plant and Leaf Separation: Application in 3D Meshes of Wheat Plants. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). Gold Coast, Queensland: IEEE.

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Discriminative Hierarchical Rank Pooling for Activity Recognition

*Fernando, B., Anderson, P., Hutter, M., & Gould, S. (2016). Discriminative hierarchical rank pooling for activity recognition. Proc. CVPR.

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The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results

*Felsberg, M., Berg, A., Häger, G., Ahlberg, J., Kristan, M., Matas, J., et.al. (2016). The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results. In 15th IEEE International Conference on Computer Vision Workshops (pp. 639–651). Santiago, Chile: Institute of Electrical and Electronics Engineers Inc.

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A Consensus-Based Framework for Distributed Bundle Adjustment

Eriksson, A., Bastian, J., Chin, T., & Isaksson, M. (2016). A Consensus-Based Framework for Distributed Bundle Adjustment. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016. Las Vegas, Nevada.

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Autonomous Greenhouse Gas Sampling Using Multiple Robotic Boats

Dunbabin, M. (2016). Autonomous greenhouse gas sampling using multiple robotic boats. In 10th International Conference on Field and Service Robotics, FSR 2015 (Vol. 113, pp. 17–30). Toronto, Canada: Springer Verlag.

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Reliable Scale Estimation and Correction for Monocular Visual Odometry

*Dingfu Zhou, Dai, Y., & Hongdong Li. (2016). Reliable scale estimation and correction for monocular Visual Odometry. In 2016 IEEE Intelligent Vehicles Symposium (IV) (pp. 490–495). Gothenburg, Sweden: IEEE.

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MO-SLAM: Multi Object SLAM with Run-Time Object Discovery through Duplicates

*Dharmasiri, T., Lui, V., & Drummond, T. (2016). MO-SLAM: Multi object SLAM with run-time object discovery through duplicates - IEEE Xplore Document. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Daejeon, Korea.

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Output Regulation on the Special Euclidean Group

de Marco, S., Marconi, L., Hamel, T., & Mahony, R. (2016). Output regulation on the Special Euclidean Group SE(3). In 2016 IEEE 55th Conference on Decision and Control (CDC) (pp. 4734–4739). IEEE.

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Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry

*Dai, Y., Li, H., & Kneip, L. (2016). Rolling shutter camera relative pose: Generalized epipolar geometry. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 4132–4140). Las Vegas, United States: IEEE Computer Society.

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Simultaneous Correspondences Estimation and Non-Rigid Structure Reconstruction

*Dai, Y., & Li, H. (2016). Simultaneous Correspondences Estimation and Non-Rigid Structure Reconstruction. In 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016. Gold Coast, Queensland: Institute of Electrical and Electronics Engineers Inc.

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Guaranteed Outlier Removal With Mixed Integer Linear Programs

*Chin, T.-J., Heng Kee, Y., Eriksson, A., & Neumann, F. (2016). Guaranteed Outlier Removal With Mixed Integer Linear Programs. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 5858–5866). Las Vegas, Nevada.

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A Distributed Robotic Vision Service

*Chamberlain, W., Leitner, J., Drummond, T., & Corke, P. (2016). A Distributed Robotic Vision Service. In IEEE International Conference on Robotics and Automation (ICRA 2016). Stockholm, Sweden.

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Dynamic Image Networks for Action Recognition

*Bilen, H., Fernando, B., Gavves, E., & Vedaldi, A. (2016). Dynamic image networks for action recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016.

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ALExTRAC: Affinity Learning by Exploring Temporal Reinforcement within Association Chains

*Bewley, A., Ott, L., Ramos, F., & Upcroft, B. (2016). Alextrac: Affinity learning by exploring temporal reinforcement within association chains. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2212–2218). Stockholm, Sweden: IEEE.

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Simple Online and Realtime Tracking

*Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016). Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 3464–3468). IEEE.

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Non-Invasive Performance Measurement in Combat Sports

Behendi, S. K., Morgan, S., & Fookes, C. B. (2016). Non-Invasive Performance Measurement in Combat Sports. In Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS) (pp. 3–10). Springer, Cham.

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SPICE: Semantic Propositional Image Caption Evaluation

*Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016). SPICE: Semantic Propositional Image Caption Evaluation. In Computer Vision - ECCV 2016 (pp. 382–398). Springer International Publishing.

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Velocity Aided Attitude Estimation for Aerial Robotic Vehicles Using Latent Rotation Scaling

Allibert, G., Mahony, R., & Bangura, M. (2016). Velocity Aided Attitude Estimation for Aerial Robotic Vehicles Using Latent Rotation Scaling. In IEEE International Conference on Robotics and Automation (ICRA 2016).

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Complex Event Detection using Joint Max Margin and Semantic Features

Abbasnejad, I., Sridharan, S., Denman, S., Fookes, C., & Lucey, S. (2016). Complex event detection using joint max margin and semantic features. In Digital Image Computing: Techniques and Applications (DICTA). Gold Coast, Queensland.

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Memory Efficient Max Flow for Multi-label Submodular MRFs

*Ajanthan, T., Hartley, R., & Salzmann, M. (2016). Memory efficient max flow for multi-label submodular MRFs. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 5867–5876). Las Vegas, Nevada: IEEE Computer Society.

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Dictionary Learning for Promoting Structured Sparsity in Hyprspectral Compressive Sensing

*Zhang, L., Wei, W., Zhang, Y., & Shen, C. (2016). Dictionary Learning for Promoting Structured Sparsity in Hyprspectral Compressive Sensing. IEEE Transactions on GeoScience and Remote Sensing, 54(12), pp.7223–7235.

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Unsupervised Feature Learning for Dense Correspondences Across Scenes

Zhang, C., Shen, C., & Shen, T. (2015). Unsupervised Feature Learning for Dense Correspondences Across Scenes. International Journal of Computer Vision, 116(1), pp.90–107.

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Development of a Multi-Channel Concentric Tube Robotic System With Active Vision for Transnasal Nasopharyngeal Carcinoma Procedures

Yu, H., Wu, L., Wu, K., & Ren, H. (2016). Development of a Multi-Channel Concentric Tube Robotic System With Active Vision for Transnasal Nasopharyngeal Carcinoma Procedures. IEEE Robotics and Automation Letters, 1(2), pp.1172–1178.

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Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration

*Yang, J., Li, H., Campbell, D., & Jia, Y. (2016). Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), pp.2241–2254.

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Detecting Rare Events Using Kullback–Leibler Divergence: A Weakly Supervised Approach

Xu, J., Denman, S., Fookes, C., & Sridharan, S. (2016). Detecting rare events using Kullback–Leibler divergence: A weakly supervised approach. Expert Systems with Applications, 54, pp.13–28.

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Hypergraph Modelling for Geometric Model Fitting

Xiao, G., Wang, H., Lai, T., & Suter, D. (2016). Hypergraph modelling for geometric model fitting. Pattern Recognition, 60, pp.748–760.

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Simultaneous Hand–Eye, Tool–Flange, and Robot–Robot Calibration for Comanipulation by Solving the Problem

Wu, L., Wang, J., Qi, L., Wu, K., Ren, H., & Meng, M. Q.-H. (2016). Simultaneous Hand–Eye, Tool–Flange, and Robot–Robot Calibration for Comanipulation by Solving the Problem. IEEE Transactions on Robotics, 32(2), pp.413–428.

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Long-Range Stereo Visual Odometry for Extended Altitude Flight of Unmanned Aerial Vehicles

*Warren, M., Corke, P., & Upcroft, B. (2016). Long-range stereo visual odometry for extended altitude flight of unmanned aerial vehicles. The International Journal of Robotics Research, 35(4), pp.381–403.

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Fast Depth Video Compression for Mobile RGB-D Sensors

*Wang, X., Sekercioglu, Y. A., Drummond, T., Natalizio, E., Fantoni, I., & Fremont, V. (2016). Fast Depth Video Compression for Mobile RGB-D Sensors. IEEE Transactions on Circuits and Systems for Video Technology, 26(4), pp.673–686.

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Correspondence Driven Saliency Transfer

*Wang, W., Shen, J., Shao, L., & Porikli, F. (2016). Correspondence Driven Saliency Transfer. IEEE Transactions on Image Processing, 25(11), pp.5025–5034.

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Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference

*Wang, P., Shen, C., van den Hengel, A., & Torr, P. H. S. (2015). Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference. International Journal of Computer Vision, 117(3), pp.269–289.

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Robust Model Fitting Using Higher Than Minimal Subset Sampling

Tennakoon, R. B., Bab-Hadiashar, A., Cao, Z., Hoseinnezhad, R., & Suter, D. (2016). Robust Model Fitting Using Higher Than Minimal Subset Sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), pp.350–362.

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Teaching Robots Generalizable Hierarchical Tasks Through Natural Language Instruction

Suddrey, G., Lehnert, C., Eich, M., Maire, F., & Roberts, J. (2016). Teaching Robots Generalisable Hierarchical Tasks Through Natural Language Instruction. IEEE Robotics and Automation Letters, 2(1), pp.201–208.

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Unlocking Neural Complexity with a Robotic Key

Stratton, P., Hasselmo, M., & Milford, M. (2016). Unlocking neural complexity with a robotic key. The Journal of Physiology, 594(22), pp.6559–6567.

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A Passivity-Based Approach to Formation Control Using Partial Measurements of Relative Position

Stacey, G., & Mahony, R. (2016). A Passivity-Based Approach to Formation Control Using Partial Measurements of Relative Position. IEEE Transactions on Automatic Control, 61(2), pp.538–543.

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Distributed Formation Control of Networked Mobile Robots in Environments with Obstacles

Seng, W. L., Barca, J. C., Şekercioğlu, Y. A., & Ahmet Ekercio˘ Glu, Y. (2016). Distributed formation control of networked mobile robots in environments with obstacles. Robotica Robotica Robotica, 34(34), pp.1403–1415.

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Strategies for Pre-Emptive Mid-Air Collision Avoidance in Budgerigars

Schiffner, I., Perez, T., Srinivasan, M. V., Angelov, P., Padian, K., Chiappe, L., et.al. (2016). Strategies for Pre-Emptive Mid-Air Collision Avoidance in Budgerigars. PLOS ONE, 11(9), e0162435.

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Deep Learning for Automatic Detection and Classification of Microaneurysms, Hard and Soft Exudates, and Hemorrhages for Diabetic Retinopathy Diagnosis

Saha, S. K., Fernando, B., Xiao, D., Tay-Kearney, M.-L., & Kanagasingam, Y. (2016). Deep Learning for Automatic Detection and Classification of Microaneurysms, Hard and Soft Exudates, and Hemorrhages for Diabetic Retinopathy Diagnosis. Investigative Ophthalmology & Visual Science, 57(12), pp.5962–5962.

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DeepFruits: A Fruit Detection System Using Deep Neural Networks Inkyu Sa *, Zongyuan Ge, Feras Dayoub, Ben Upcroft, Tristan Perez and Chris McCool Science and Engineering Faculty, Queensland University of Technology, Brisbane 4000, Australia * Correspondence: Tel.: +61-449-722-415 Academic Editors: Gabriel Oliver-Codina, Nuno Gracias and Antonio M. López Received: 19 May 2016 / Accepted: 26 July 2016 / Published: 3 August 2016

*Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors, 16(8), pp.1222.

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A Flexible Hierarchical Approach For Facial Age Estimation Based on Multiple Features

Pontes, J. K., Britto, A. S., Fookes, C., & Koerich, A. L. (2016). A flexible hierarchical approach for facial age estimation based on multiple features. Pattern Recognition, 54, pp.34–51.

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Routed Roads: Probabilistic Vision-Based Place Recognition for Changing Conditions, Split Streets and Varied Viewpoints

*Pepperell, E., Corke, P., & Milford, M. (2016). Routed roads: Probabilistic vision-based place recognition for changing conditions, split streets and varied viewpoints. The International Journal of Robotics Research, 35(9), pp.1057–1079.

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Fast Rotation Search with Stereographic Projections for 3D Registration

*Parra Bustos, A., Chin, T.-J., Eriksson, A., Li, H., & Suter, D. (2016). Fast Rotation Search with Stereographic Projections for 3D Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), pp.2227–2240.

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Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning

*Paisitkriangkrai, S., Shen, C., & Hengel, A. van den. (2016). Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(6), pp.1243–1257.

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Contour Completion Without Region Segmentation

*Ming, Y., Li, H., & He, X. (2016). Contour Completion Without Region Segmentation. IEEE Transactions on Image Processing, 25(8), pp.3597–3611.

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RatSLAM: Using Models of Rodent Hippocampus for Robot Navigation and Beyond

*Milford, M., Jacobson, A., Chen, Z., & Wyeth, G. (2016). RatSLAM: Using Models of Rodent Hippocampus for Robot Navigation and Beyond. In Robotics Research (pp. 467–485).

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Visual Tracking Under Motion Blur

*Ma, B., Huang, L., Shen, J., Shao, L., Yang, M.-H., & Porikli, F. (2016). Visual Tracking Under Motion Blur. IEEE Transactions on Image Processing, 25(12), pp.5867–5876.

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Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments

*Lowry, S., & Milford, M. J. (2016). Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments. IEEE Transactions on Robotics, 32(3), pp.600–613.

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A Generalized Probabilistic Framework for Compact Codebook Creation

*Liu, L., Wang, L., & Shen, C. (2016). A Generalized Probabilistic Framework for Compact Codebook Creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), pp.224–37.

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Online Unsupervised Feature Learning for Visual Tracking

*Liu, F., Shen, C., Reid, I., & van den Hengel, A. (2016). Online unsupervised feature learning for visual tracking. Image and Vision Computing, 51(July), pp.84–94.

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Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

*Liu, F., Shen, C., Lin, G., & Reid, I. (2016). Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(10), pp.2024–2039.

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Online Metric-Weighted Linear Representations for Robust Visual Tracking

*Li, X., Shen, C., Dick, A., Zhang, Z. M., & Zhuang, Y. (2016). Online Metric-Weighted Linear Representations for Robust Visual Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(5), pp.931–950.

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Convolutional Neural Net Bagging for Online Visual Tracking

*Li, H., Li, Y., & Porikli, F. (2016). Convolutional neural net bagging for online visual tracking. Computer Vision and Image Understanding, 153 (December 2016), pp.120–129.

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A Modular Software Framework for Eye–Hand Coordination in Humanoid Robots

*Leitner, J., Harding, S., Förster, A., & Corke, P. (2016). A Modular Software Framework for Eye–Hand Coordination in Humanoid Robots. Frontiers in Robotics and AI, 3, 26.

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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. *Article in press

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A Novel Performance Evaluation Methodology for Single-Target Trackers

*Kristan, M., Matas, J., Leonardis, A., Vojir, T., Pflugfelder, R., Fernandez, G., et.al. (2016). A Novel Performance Evaluation Methodology for Single-Target Trackers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), pp.2137–2155.

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State Estimation for Invariant Systems on Lie Groups with Delayed Output Measurements

Khosravian, A., Trumpf, J., Mahony, R., & Hamel, T. (2016). State estimation for invariant systems on Lie groups with delayed output measurements. Automatica, 68, pp.254–265.

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Fast Detection of Multiple Objects in Traffic Scenes with a Common Detection Framework

*Hu, Q., Paisitkriangkrai, S., Shen, C., van den Hengel, A., & Porikli, F. (2016). Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Transactions on Intelligent Transportation Systems, 17(4), pp.1002–1014.

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Dynamic Kinesthetic Boundary for Haptic Teleoperation of VTOL Aerial Robots in Complex Environments

Hou, X., & Mahony, R. (2016). Dynamic Kinesthetic Boundary for Haptic Teleoperation of VTOL Aerial Robots in Complex Environments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(5), pp.694–705.

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Automated Fourier Space Region-Recognition Filtering for Off-Axis Digital Holographic Microscopy

*He, X., Nguyen, C. V., Pratap, M., Zheng, Y., Wang, Y., Nisbet, D. R., et.al. (2016). Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy. Biomedical Optics Express, 7(8), pp.3111.

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Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences

Harandi, M. T., Hartley, R., Lovell, B., & Sanderson, C. (2016). Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences. IEEE Transactions on Neural Networks and Learning Systems, 27(6), pp.1294–1306.

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Partitioning de Bruijn Graphs into Fixed-Length Cycles for Robot Identification and Tracking

Grubman, T., Şekercioğlu, Y. A., & Wood, D. R. (2016). Partitioning de Bruijn graphs into fixed-length cycles for robot identification and tracking. Discrete Applied Mathematics, 213, pp.101–113.

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Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

*Ferrari, R., McKinnon, D., He, H., Smith, R., Corke, P., González-Rivero, M., et.al. (2016). Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling. Remote Sensing, 8(2), pp.113.

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Simple Change Detection from Mobile Light Field Cameras

*Dansereau, D. G., Williams, S. B., & Corke, P. I. (2016). Simple change detection from mobile light field cameras. Computer Vision and Image Understanding, 145(April), pp.160–171.

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Bayesian Nonparametric Clustering for Positive Definite Matrices

Cherian, A., Morellas, V., & Papanikolopoulos, N. (2016). Bayesian Nonparametric Clustering for Positive Definite Matrices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(5), pp.862–74.

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Measuring the Performance of Single Image Depth Estimation Methods

*Cadena, C., Latif, Y., & Reid, I. D. (2016). Measuring the performance of single image depth estimation methods. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4150–4157). Daejeon, Korea: IEEE.

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Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

*Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., et.al. Leonard, J. J. (2016). Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age. IEEE Transactions on Robotics, 32(6), pp.1309–1332.

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Discovering Team Structures in Soccer from Spatiotemporal Data

Bialkowski, A., Lucey, P., Carr, P., Matthews, I., Sridharan, S., & Fookes, C. (2016). Discovering Team Structures in Soccer from Spatiotemporal Data. IEEE Transactions on Knowledge and Data Engineering, 28(10), pp.2596–2605.

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From ImageNet to Mining: Adapting Visual Object Detection with Minimal Supervision

*Bewley, A., & Upcroft, B. (2016). From imagenet to mining: Adapting visual object detection with minimal supervision. In 10th International Conference on Field and Service Robotics, FSR 2015; (Vol. 113, pp. 501–514). Toronto, Canada: Springer Verlag.

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Vision-based Obstacle Detection and Navigation for an Agricultural Robot

*Ball, D., Upcroft, B., Wyeth, G., Corke, P., English, A., Ross, P., et.al. (2016). Vision-based Obstacle Detection and Navigation for an Agricultural Robot. Journal of Field Robotics, 33(8), pp.1107–1130.

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A Filter Formulation for Computing Real Time Optical Flow

*Adarve, J. D., & Mahony, R. (2016). A Filter Formulation for Computing Real Time Optical Flow. IEEE Robotics and Automation Letters, 1(2), pp.1192–1199.

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Dictionary Learning for Promoting Structured Sparsity in Hyprspectral Compressive Sensing

*Zhang, L., Wei, W., Zhang, Y., & Shen, C. (2016). Dictionary Learning for Promoting Structured Sparsity in Hyprspectral Compressive Sensing. IEEE Transactions on GeoScience and Remote Sensing, 54(12), pp.7223–7235.

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Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration

*Yang, J., Li, H., Campbell, D., & Jia, Y. (2016). Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), pp.2241–2254.

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Detecting rare events using Kullback–Leibler divergence: A weakly supervised approach

Xu, J., Denman, S., Fookes, C., & Sridharan, S. (2016). Detecting rare events using Kullback–Leibler divergence: A weakly supervised approach. Expert Systems with Applications, 54, pp.13–28.

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Hypergraph modelling for geometric model fitting

Xiao, G., Wang, H., Lai, T., & Suter, D. (2016). Hypergraph modelling for geometric model fitting. Pattern Recognition, 60, pp.748–760.

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Long-range stereo visual odometry for extended altitude flight of unmanned aerial vehicles

*Warren, M., Corke, P., & Upcroft, B. (2016). Long-range stereo visual odometry for extended altitude flight of unmanned aerial vehicles. The International Journal of Robotics Research, 35(4), pp.381–403.

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Fast Depth Video Compression for Mobile RGB-D Sensors

*Wang, X., Sekercioglu, Y. A., Drummond, T., Natalizio, E., Fantoni, I., & Fremont, V. (2016). Fast Depth Video Compression for Mobile RGB-D Sensors. IEEE Transactions on Circuits and Systems for Video Technology, 26(4), pp.673–686.

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Correspondence Driven Saliency Transfer

*Wang, W., Shen, J., Shao, L., & Porikli, F. (2016). Correspondence Driven Saliency Transfer. IEEE Transactions on Image Processing, 25(11), pp.5025–5034.

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Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference

*Wang, P., Shen, C., van den Hengel, A., & Torr, P. H. S. (2015). Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference. International Journal of Computer Vision, 117(3), pp.269–289.

View more

Robust Model Fitting Using Higher Than Minimal Subset Sampling

Tennakoon, R. B., Bab-Hadiashar, A., Cao, Z., Hoseinnezhad, R., & Suter, D. (2016). Robust Model Fitting Using Higher Than Minimal Subset Sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), pp.350–362.

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Teaching Robots Generalisable Hierarchical Tasks Through Natural Language Instruction

Suddrey, G., Lehnert, C., Eich, M., Maire, F., & Roberts, J. (2016). Teaching Robots Generalisable Hierarchical Tasks Through Natural Language Instruction. IEEE Robotics and Automation Letters, 2(1), pp.201–208.

View more

Unlocking neural complexity with a robotic key

Stratton, P., Hasselmo, M., & Milford, M. (2016). Unlocking neural complexity with a robotic key. The Journal of Physiology, 594(22), pp.6559–6567.

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A Passivity-Based Approach to Formation Control Using Partial Measurements of Relative Position

Stacey, G., & Mahony, R. (2016). A Passivity-Based Approach to Formation Control Using Partial Measurements of Relative Position. IEEE Transactions on Automatic Control, 61(2), pp.538–543.

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Distributed formation control of networked mobile robots in environments with obstacles

Seng, W. L., Barca, J. C., Şekercioğlu, Y. A., & Ahmet Ekercio˘ Glu, Y. (2016). Distributed formation control of networked mobile robots in environments with obstacles. Robotica Robotica Robotica, 34(34), pp.1403–1415.

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Strategies for Pre-Emptive Mid-Air Collision Avoidance in Budgerigars

Schiffner, I., Perez, T., Srinivasan, M. V., Angelov, P., Padian, K., Chiappe, L., et.al. (2016). Strategies for Pre-Emptive Mid-Air Collision Avoidance in Budgerigars. PLOS ONE, 11(9), e0162435.

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Deep Learning for Automatic Detection and Classification of Microaneurysms, Hard and Soft Exudates, and Hemorrhages for Diabetic Retinopathy Diagnosis

Saha, S. K., Fernando, B., Xiao, D., Tay-Kearney, M.-L., & Kanagasingam, Y. (2016). Deep Learning for Automatic Detection and Classification of Microaneurysms, Hard and Soft Exudates, and Hemorrhages for Diabetic Retinopathy Diagnosis. Investigative Ophthalmology & Visual Science, 57(12), pp.5962–5962.

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DeepFruits: A Fruit Detection System Using Deep Neural Networks

*Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors, 16(8), pp.1222.

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A flexible hierarchical approach for facial age estimation based on multiple features

Pontes, J. K., Britto, A. S., Fookes, C., & Koerich, A. L. (2016). A flexible hierarchical approach for facial age estimation based on multiple features. Pattern Recognition, 54, pp.34–51.

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Routed roads: Probabilistic vision-based place recognition for changing conditions, split streets and varied viewpoints

*Pepperell, E., Corke, P., & Milford, M. (2016). Routed roads: Probabilistic vision-based place recognition for changing conditions, split streets and varied viewpoints. The International Journal of Robotics Research, 35(9), pp.1057–1079.

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Fast Rotation Search with Stereographic Projections for 3D Registration

*Parra Bustos, A., Chin, T.-J., Eriksson, A., Li, H., & Suter, D. (2016). Fast Rotation Search with Stereographic Projections for 3D Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), pp.2227–2240.

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Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning

*Paisitkriangkrai, S., Shen, C., & Hengel, A. van den. (2016). Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(6), pp.1243–1257.

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Contour Completion Without Region Segmentation

*Ming, Y., Li, H., & He, X. (2016). Contour Completion Without Region Segmentation. IEEE Transactions on Image Processing, 25(8), pp.3597–3611.

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RatSLAM: Using Models of Rodent Hippocampus for Robot Navigation and Beyond

*Milford, M., Jacobson, A., Chen, Z., & Wyeth, G. (2016). RatSLAM: Using Models of Rodent Hippocampus for Robot Navigation and Beyond. In Robotics Research (pp. 467–485).

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Visual Tracking Under Motion Blur

*Ma, B., Huang, L., Shen, J., Shao, L., Yang, M.-H., & Porikli, F. (2016). Visual Tracking Under Motion Blur. IEEE Transactions on Image Processing, 25(12), pp.5867–5876.

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Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments

*Lowry, S., & Milford, M. J. (2016). Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments. IEEE Transactions on Robotics, 32(3), pp.600–613.

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A Generalized Probabilistic Framework for Compact Codebook Creation

*Liu, L., Wang, L., & Shen, C. (2016). A Generalized Probabilistic Framework for Compact Codebook Creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), pp.224–37.

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Online unsupervised feature learning for visual tracking

*Liu, F., Shen, C., Reid, I., & van den Hengel, A. (2016). Online unsupervised feature learning for visual tracking. Image and Vision Computing, 51(July), pp.84–94.

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Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

*Liu, F., Shen, C., Lin, G., & Reid, I. (2016). Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(10), pp.2024–2039.

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Online Metric-Weighted Linear Representations for Robust Visual Tracking

*Li, X., Shen, C., Dick, A., Zhang, Z. M., & Zhuang, Y. (2016). Online Metric-Weighted Linear Representations for Robust Visual Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(5), pp.931–950.

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Convolutional neural net bagging for online visual tracking

*Li, H., Li, Y., & Porikli, F. (2016). Convolutional neural net bagging for online visual tracking. Computer Vision and Image Understanding, 153 (December 2016), pp.120–129.

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A Modular Software Framework for Eye–Hand Coordination in Humanoid Robots

*Leitner, J., Harding, S., Förster, A., & Corke, P. (2016). A Modular Software Framework for Eye–Hand Coordination in Humanoid Robots. Frontiers in Robotics and AI, 3, 26.

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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. *Article in press

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A Novel Performance Evaluation Methodology for Single-Target Trackers

*Kristan, M., Matas, J., Leonardis, A., Vojir, T., Pflugfelder, R., Fernandez, G., et.al. (2016). A Novel Performance Evaluation Methodology for Single-Target Trackers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), pp.2137–2155.

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State estimation for invariant systems on Lie groups with delayed output measurements

Khosravian, A., Trumpf, J., Mahony, R., & Hamel, T. (2016). State estimation for invariant systems on Lie groups with delayed output measurements. Automatica, 68, pp.254–265.

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Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework

*Hu, Q., Paisitkriangkrai, S., Shen, C., van den Hengel, A., & Porikli, F. (2016). Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Transactions on Intelligent Transportation Systems, 17(4), pp.1002–1014.

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Dynamic Kinesthetic Boundary for Haptic Teleoperation of VTOL Aerial Robots in Complex Environments

Hou, X., & Mahony, R. (2016). Dynamic Kinesthetic Boundary for Haptic Teleoperation of VTOL Aerial Robots in Complex Environments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(5), pp.694–705.

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Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy

*He, X., Nguyen, C. V., Pratap, M., Zheng, Y., Wang, Y., Nisbet, D. R., et.al. (2016). Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy. Biomedical Optics Express, 7(8), pp.3111.

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Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences

*Harandi, M. T., Hartley, R., Lovell, B., & Sanderson, C. (2016). Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences. IEEE Transactions on Neural Networks and Learning Systems, 27(6), pp.1294–1306.

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Partitioning de Bruijn graphs into fixed-length cycles for robot identification and tracking

Grubman, T., Şekercioğlu, Y. A., & Wood, D. R. (2016). Partitioning de Bruijn graphs into fixed-length cycles for robot identification and tracking. Discrete Applied Mathematics, 213, pp.101–113.

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Discovering Team Structures in Soccer from Spatiotemporal Data

Bialkowski, A., Lucey, P., Carr, P., Matthews, I., Sridharan, S., & Fookes, C. (2016). Discovering Team Structures in Soccer from Spatiotemporal Data. IEEE Transactions on Knowledge and Data Engineering, 28(10), pp.2596–2605.

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From imagenet to mining: Adapting visual object detection with minimal supervision

*Bewley, A., & Upcroft, B. (2016). From imagenet to mining: Adapting visual object detection with minimal supervision. In 10th International Conference on Field and Service Robotics, FSR 2015; (Vol. 113, pp. 501–514). Toronto, Canada: Springer Verlag.

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Memory efficient max flow for multi-label submodular MRFs.

*Ajanthan, T., Hartley, R., & Salzmann, M. (2016). Memory efficient max flow for multi-label submodular MRFs. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 5867–5876). Las Vegas, Nevada: IEEE Computer Society.

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Vision-based Obstacle Detection and Navigation for an Agricultural Robot

*Ball, D., Upcroft, B., Wyeth, G., Corke, P., English, A., Ross, P., et.al. (2016). Vision-based Obstacle Detection and Navigation for an Agricultural Robot. Journal of Field Robotics, 33(8), pp.1107–1130.

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A Filter Formulation for Computing Real Time Optical Flow

*Adarve, J. D., & Mahony, R. (2016). A Filter Formulation for Computing Real Time Optical Flow. IEEE Robotics and Automation Letters, 1(2), pp.1192–1199.

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Robotics Competitions and Challenges

Nardi, D., Roberts, J., Veloso, M., & Fletcher, L. (2016). Robotics Competitions and Challenges. In Springer Handbook of Robotics (pp. 1759–1788). Cham: Springer International Publishing.

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Modeling and Control of Aerial Robots

Mahony, R., Beard, R. W., & Kumar, V. (2016). Modeling and Control of Aerial Robots. In Springer Handbook of Robotics (pp. 1307–1334). Springer International Publishing.

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Visual Servoing

*Chaumette, F., Hutchinson, S., & Corke, P. (2016). Visual Servoing. In Springer Handbook of Robotics (pp. 841–866). Springer International Publishing.

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