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2017 Scientific Publications [57]

FPGA acceleration of multilevel ORB feature extraction for computer vision

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

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Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation

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

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A compact parametric solution to depth sensor calibration

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

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Thrust Control for Multirotor Aerial Vehicles

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

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

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

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

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

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Joint Dimensionality Reduction and Metric Learning: A Geometric Take

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

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Mission-time 3D reconstruction with quality estimation

Istenic, K., Ila, V., Polok, L., Gracias, N., & Garcia, R. (2017). Mission-time 3D reconstruction with quality estimation. In OCEANS 2017 - Aberdeen (pp. 1–9). Aberdeen, UK: IEEE. http://doi.org/10.1109/OCEANSE.2017.8084708

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

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

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

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

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

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

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Quickest detection of intermittent signals with estimated anomaly times

James, J., Ford, J. J., & Molloy, T. L. (2017). Quickest detection of intermittent signals with estimated anomaly times. In 2017 Asian Control Conference - ASCC 2017. Gold Coast. Retrieved from https://eprints.qut.edu.au/112160/

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

Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2017). Going deeper: Autonomous steering with neural memory networks. In IEEE Conference on Computer Vision and Pattern Recognition. Hawaii. Retrieved from https://eprints.qut.edu.au/114117/

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

Bista, S. R., Giordano, P. R., & Chaumette, F. (2017). Combining Line Segments and Points for Appearance- based Indoor Navigation by Image Based Visual Servoing. In IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2960–2967). Vancouver. Retrieved from https://hal.inria.fr/hal-01572353/

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

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

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

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

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Robot for weed species plant-specific management

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

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Biologically-inspired visual place recognition with adaptive multiple scales

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

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

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

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Improved Semantic segmentation for robotic applications with hierarchical conditional random fields

Meyer, B.J., Drummond, T. (2017). Improved Semantic segmentation for robotic applications with hierarchical conditional random fields. 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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Training Improves Vibrotactile Spatial Acuity and Intensity Discrimination on the Lower Back Using Coin Motors

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

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

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

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Ordered Pooling of Optical Flow Sequences for Action Recognition

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

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

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

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

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

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

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

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A converse to the deterministic separation principle

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

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Dense monocular reconstruction using surface normals

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

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

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

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Standard operating procedures for UAV or drone basedmonitoring of wildlife

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

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

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

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

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

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

Bratanov, D., Mejias, L., & Ford, J. J. (2017). A vision-based sense-and-avoid system tested on a ScanEagle UAV. International Conference on Unmanned Aerial Systems (ICUAS) 2017. Retrieved from https://eprints.qut.edu.au/108459/

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Rank Pooling for Action Recognition

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

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

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

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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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Observers for Position Estimation Using Bearing and Biased Velocity Information

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

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

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

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Introduction to the special section on Artificial Intelligence and Computer Vision

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

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

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

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

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

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Autonomous Sweet Pepper Harvesting for Protected Cropping Systems

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

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

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

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

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

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

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

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

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

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

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

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

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

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Image-Based Visual Servoing With Unknown Point Feature Correspondence

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

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Image-Based Visual Servoing With Light Field Cameras

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

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Spherepix: A Data Structure for Spherical Image Processing

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

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