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Publications

2017 Submitted [30]

Coresets for Triangulation

Zhang, Q., & Chin, T.-J. (2017). Coresets for Triangulation. Retrieved from http://arxiv.org/abs/1707.05466

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Low-Rank Kernel Subspace Clustering

Ji, P., Reid, I., Garg, R., Li, H., & Salzmann, M. (2017). Low-Rank Kernel Subspace Clustering. Retrieved from http://arxiv.org/abs/1707.04974

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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. Retrieved from http://arxiv.org/abs/1707.05009

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Visually Aligned Word Embeddings for Improving Zero-shot Learning

Qiao, R., Liu, L., Shen, C., & Hengel, A. van den. (2017). Visually Aligned Word Embeddings for Improving Zero-shot Learning. Retrieved from http://arxiv.org/abs/1707.05427

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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. Retrieved from https://arxiv.org/abs/1706.07593

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Multi-Modal Trip Hazard Affordance Detection On Construction Sites

McMahon, S. M., Sunderhauf, N., Upcroft, B., & Milford, M. J. (2017). Multi-Modal Trip Hazard Affordance Detection On Construction Sites. IEEE Robotics and Automation Letters. http://doi.org/10.1109/LRA.2017.2719763

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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. Retrieved from http://arxiv.org/abs/1706.07144

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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. International Conference on Unmanned Aerial Systems (ICUAS) 2017. Retrieved from https://eprints.qut.edu.au/108459/

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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 *In Press

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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. Retrieved from http://arxiv.org/abs/1707.00381

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Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features

Shigematsu, R., Feng, D., You, S., & Barnes, N. (2017). Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features. Retrieved from https://arxiv.org/pdf/1705.03607.pdf

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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. Retrieved from http://arxiv.org/abs/1701.04175

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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. Retrieved from http://arxiv.org/abs/1705.05116

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Sequence Summarization Using Order-constrained Kernelized Feature Subspaces

Cherian, A., Sra, S., & Hartley, R. (2017). Sequence Summarization Using Order-constrained Kernelized Feature Subspaces. Retrieved from https://arxiv.org/pdf/1705.08583.pdf

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Weakly Supervised Semantic Segmentation Based on Co-segmentation

Shen, T., Lin, G., Liu, L., Shen, C., & Reid, I. (2017). Weakly Supervised Semantic Segmentation Based on Co-segmentation. Retrieved from http://arxiv.org/abs/1705.09052

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Nearest Neighbour Radial Basis Function Solvers for Deep Neural Networks

Meyer, B. J., Harwood, B., & Drummond, T. (2017). Nearest Neighbour Radial Basis Function Solvers for Deep Neural Networks. Retrieved from http://arxiv.org/abs/1705.09780

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Discriminatively Learned Hierarchical Rank Pooling Networks

Fernando, B., & Gould, S. (2017). Discriminatively Learned Hierarchical Rank Pooling Networks. Retrieved from http://arxiv.org/abs/1705.10420

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Care about you: towards large-scale human-centric visual relationship detection

Zhuang, B., Wu, Q., Shen, C., Reid, I., & Hengel, A. van den. (2017). Care about you: towards large-scale human-centric visual relationship detection. Retrieved from http://arxiv.org/abs/1705.09892

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Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

Leal-Taixé, L., Milan, A., Schindler, K., Cremers, D., Reid, I., & Roth, S. (2017). Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking. Retrieved from http://arxiv.org/abs/1704.02781

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Smart Mining for Deep Metric Learning

Kumar, V. B. G., Harwood, B., Carneiro, G., Reid, I., & Drummond, T. (2017). Smart Mining for Deep Metric Learning. Retrieved from http://arxiv.org/abs/1704.01285

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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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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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Inverse noncooperative dynamic games

Molloy, T. L., Ford, J. J., & Perez, T. (2017). Inverse noncooperative dynamic games. In 20th World Congress of the International Federation of Automatic Control (IFAC 2017). Toulouse, France. Retrieved from http://eprints.qut.edu.au/105144/

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What Would You Do? Acting by Learning to Predict

Tow, A., Sünderhauf, N., Shirazi, S., Milford, M., & Leitner, J. (2017). What Would You Do? Acting by Learning to Predict. Retrieved from http://arxiv.org/abs/1703.02658

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Episode-Based Active Learning with Bayesian Neural Networks

Dayoub, F., Sünderhauf, N., & Corke, P. (2017). Episode-Based Active Learning with Bayesian Neural Networks. Retrieved from http://arxiv.org/abs/1703.07473

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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. Retrieved from http://arxiv.org/abs/1702.01247

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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. Retrieved from http://arxiv.org/abs/1701.04175

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Action Recognition: From Static Datasets to Moving Robots

Rezazadegan, F., Shirazi, S., Upcroft, B., & Milford, M. (2017). Action Recognition: From Static Datasets to Moving Robots. Retrieved from http://arxiv.org/abs/1701.04925

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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. Retrieved from http://arxiv.org/abs/1701.05105

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