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2017 All Categories [88]

Semantic Segmentation from Limited Training Data

Milan, A., Pham, T., Vijay, K., Morrison, D., Tow, A. W., Liu, L., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Kelly-Boxall, K., Lee, D., McTaggart, M., Rallos, G., Razjigaev, A., Rowntree, T., Shen, T., Smith, R., Wade-McCue, S., Zhuang, Z., Lehnert, C., Lin, G., Reid, I., Corke, P., and Leitner, J. (2017). Semantic Segmentation from Limited Training Data. Retrieved from https://arxiv.org/abs/1709.07665

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Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge

Morrison, D., Tow, A. W., McTaggart, M., Smith, R., Kelly-Boxall, N., Wade-McCue, S., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Lee, D., Milan, A., Pham, T., Rallos, G., Razjigaev, A., Rowntree, T., Kumar, V., Zhuang, Z., Lehnert, C., Reid, I., Corke, P., and Leitner, J. (2017). Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge. Retrieved from https://arxiv.org/abs/1709.06283

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Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks

Zhang, F., Leitner, J., Milford, M., & Corke, P. (2017). Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks. Retrieved from https://arxiv.org/abs/1709.05746

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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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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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Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks

Wang, X., Sekercioglu, Y.A., Drummond, T., Fremont, V., Natalizio, E., Fantoni, I. (2017). Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks. Retrieved from arXiv preprint arXiv:1707.05978

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A Fast Method for Computing Principal Curvatures from Range Images.

Spek, A., Li, W.H., Drummond, T., (2017). A Fast Method for Computing Principal Curvatures from Range Images. Retrieved from arXiv preprint arXiv:1707.00385

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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. Robotics and Automation (ICRA), 2017 IEEE International Conference on, 5258-5265

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Solving Robust Regularization Problems Using Iteratively Re-weighted Least Squares

Kiani, KA., Drummond, T. (2017). Solving Robust Regularization Problems Using Iteratively Re-weighted Least Squares. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). 483-492. IEEE

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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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Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation

Saleh, F. S., Aliakbarian, M. S., Salzmann, M., Petersson, L., Alvarez, J. M., & Gould, S. (2017). Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation. Retrieved from https://arxiv.org/pdf/1706.02189.pdf

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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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Second-order Temporal Pooling for Action Recognition

Cherian, A., & Gould, S. (2017). Second-order Temporal Pooling for Action Recognition. Retrieved from http://arxiv.org/abs/1704.06925

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DeepPermNet: Visual Permutation Learning

Cruz, R. S., Fernando, B., Cherian, A., & Gould, S. (2017). DeepPermNet: Visual Permutation Learning. Retrieved from https://arxiv.org/abs/1704.02729

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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. Retrieved from https://arxiv.org/abs/1704.02112

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Action Representation Using Classifier Decision Boundaries

Wang, J., Cherian, A., Porikli, F., & Gould, S. (2017). Action Representation Using Classifier Decision Boundaries. Retrieved from http://arxiv.org/abs/1704.01716

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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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Encouraging LSTMs to Anticipate Actions Very Early

Aliakbarian, M. S., Saleh, F., Salzmann, M., Fernando, B., Petersson, L., & Andersson, L. (2017). Encouraging LSTMs to Anticipate Actions Very Early. Retrieved from http://arxiv.org/abs/1703.07023

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Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening

Saha, S. K., Fernando, B., Cuadros, J., Xiao, D., & Kanagasingam, Y. (2017). Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening. Retrieved from http://arxiv.org/abs/1703.02511

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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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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. Retrieved from http://arxiv.org/abs/1702.02512

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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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Simultaneous Feature Aggregating and Hashing for Large-scale Image Search

Do, T.-T., Le, D.-K., Trung, T., & Pham, T. (n.d.). Simultaneous Feature Aggregating and Hashing for Large-scale Image Search. Retrieved from https://arxiv.org/pdf/1704.00860.pdf

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Adversarial Generation of Training Examples for Vehicle License Plate Recognition

Wang, X., You, M., & Shen, C. (2017). Adversarial Generation of Training Examples for Vehicle License Plate Recognition. Retrieved from http://arxiv.org/abs/1707.03124

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Robust Visual Tracking via Hierarchical Convolutional Features

*Ma, C., Huang, J.-B., Yang, X., & Yang, M.-H. (n.d.). Robust Visual Tracking via Hierarchical Convolutional Features. Retrieved from https://arxiv.org/pdf/1707.03816.pdf

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Visual Question Answering with Memory-Augmented Networks

Ma, C., Shen, C., Dick, A., & Van Den Hengel, A. (n.d.). Visual Question Answering with Memory-Augmented Networks. Retrieved from https://arxiv.org/pdf/1707.04968.pdf

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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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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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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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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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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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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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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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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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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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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. http://doi.org/10.1016/j.patcog.2017.05.014 *In Press

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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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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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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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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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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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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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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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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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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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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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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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Background Appearance Modeling with Applications to Visual Object Detection in an Open-Pit Mine

Bewley, A., & Upcroft, B. (2017). Background Appearance Modeling with Applications to Visual Object Detection in an Open-Pit Mine. Journal of Field Robotics, 34(1), 53–73. http://doi.org/10.1002/rob.21667

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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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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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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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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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Quantifying Spatiotemporal Greenhouse Gas Emissions Using Autonomous Surface Vehicles

Dunbabin, M., & Grinham, A. (2017). Quantifying Spatiotemporal Greenhouse Gas Emissions Using Autonomous Surface Vehicles. Journal of Field Robotics, 34(1), 151–169. http://doi.org/10.1002/rob.21665

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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), 201–208. http://doi.org/10.1109/LRA.2016.2588584

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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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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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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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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.

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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.

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