|Member Login

Publications

2017 Scientific Publications [113]

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

View more

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

View more

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/

View more

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

View more

Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L. (2017). Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. Retrieved from http://arxiv.org/abs/1707.07998

View more

Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

Teney, D., Anderson, P., He, X., & Hengel, A. van den. (2017). Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge. Retrieved from http://arxiv.org/abs/1708.02711

View more

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., Reid, I., Gould, S., and Hengel, A. van den. (2017). Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments. Retrieved from http://arxiv.org/abs/1711.07280

View more

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

View more

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

View more

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.

View more

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

View more

Depth Map Completion by Jointly Exploiting Blurry Color Images and Sparse Depth Maps

Pan, L., Dai, Y., Liu, M., & Porikli, F. (2017). Depth Map Completion by Jointly Exploiting Blurry Color Images and Sparse Depth Maps. Retrieved from http://arxiv.org/abs/1711.09501

View more

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

View more

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

View more

A Real-time Action Prediction Framework by Encoding Temporal Evolution

Rezazadegan, F., Shirazi, S., & Davis, L. S. (2017). A Real-time Action Prediction Framework by Encoding Temporal Evolution. Retrieved from https://arxiv.org/abs/1709.07894

View more

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

View more

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/

View more

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/

View more

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/

View more

Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge

Wade-McCue, S., Kelly-Boxall, N., McTaggart, M., Morrison, D., Tow, A. W., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Lee, D., Milan, A., Pham, T., Rallos, G., Razjigaev, A., Rowntree, T., Smith, R., Kumar, Vijay., Zhuang, Z., Lehnert, C., Reid, I., Corke, P., and Leitner, J. (2017). Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge. Retrieved from http://arxiv.org/abs/1710.01439

View more

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

View more

SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

Pham, T., Do, T.-T., Sünderhauf, N., & Reid, I. (2017). SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes. Retrieved from http://arxiv.org/abs/1709.07158

View more

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

View more

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., Reid, I., Gould, S., and Hengel, A. van den. (2017). Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments. Retrieved from http://arxiv.org/abs/1711.07280

View more

A Transplantable System for Weed Classification by Agricultural Robotics

Hall, D., Dayoub, F., Perez, T., & Mccool, C. (2017). A Transplantable System for Weed Classification by Agricultural Robotics. Retrieved from http://www.ferasdayoub.com/wp-content/uploads/2014/12/IROS17_0494_FI.pdf

View more

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

View more

Dropout Sampling for Robust Object Detection in Open-Set Conditions

Miller, D., Nicholson, L., Dayoub, F., & Sünderhauf, N. (2017). Dropout Sampling for Robust Object Detection in Open-Set Conditions. Retrieved from http://arxiv.org/abs/1710.06677

View more

Deja vu: Scalable Place Recognition Using Mutually Supportive Feature Frequencies

Jacobson, A., Scheirer, W., & Milford, M. (2017). Deja vu: Scalable Place Recognition Using Mutually Supportive Feature Frequencies. Retrieved from http://arxiv.org/abs/1707.06393

View more

Dual Quadrics from Object Detection BoundingBoxes as Landmark Representations in SLAM

Sünderhauf, N., & Milford, M. (2017). Dual Quadrics from Object Detection BoundingBoxes as Landmark Representations in SLAM. Retrieved from http://arxiv.org/abs/1708.00965

View more

Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks

Latif, Y., Garg, R., Milford, M., & Reid, I. (2017). Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks. Retrieved from http://arxiv.org/abs/1709.08810

View more

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

View more

One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay

Bruce, J., Suenderhauf, N., Mirowski, P., Hadsell, R., & Milford, M. (2017). One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay. Retrieved from http://arxiv.org/abs/1711.10137

View more

Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost

Yu, L., Jacobson, A., & Milford, M. (2017). Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost. Retrieved from http://arxiv.org/abs/1712.07315

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

Coresets for Triangulation

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

View more

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

View more

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

View more

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

View more

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/

View more

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

View more

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

View more

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

View more

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/

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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.

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

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

View more

Australian Centre for Robotic Vision
2 George Street Brisbane, 4001
+61 7 3138 7549