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Publications

2017 Conference Papers [17]

FPGA acceleration of multilevel ORB feature extraction for computer vision

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

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Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation

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

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A compact parametric solution to depth sensor calibration

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

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Joint Dimensionality Reduction and Metric Learning: A Geometric Take

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

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

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

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Ordered Pooling of Optical Flow Sequences for Action Recognition

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

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Dense monocular reconstruction using surface normals

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

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Standard operating procedures for UAV or drone basedmonitoring of wildlife

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

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

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

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

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

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