Scientific Publications
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12th Asian conference on computer vision
Reid, I. (2016). 12th Asian conference on computer vision. In Computer Vision and Image Understanding (Vol. 146, p. 51). Academic Press Inc. https://doi.org/10.1016/j.cviu.2016.03.016
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Superpixel-based appearance change prediction for long-term navigation across seasons
Neubert, P., Sünderhauf, N., & Protzel, P. (2015). Superpixel-based appearance change prediction for long-term navigation across seasons. Robotics and Autonomous Systems, 69(1), 15–27. https://doi.org/10.1016/j.robot.2014.08.005
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A Revisit of Methods for Determining the Fundamental Matrix with Planes
Zhou, Y., Kneip, L., & Li, H. (2015). A Revisit of Methods for Determining the Fundamental Matrix with Planes. In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE. http://doi.org/10.1109/DICTA.2015.7371221
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Bags of Affine Subspaces for Robust Object Tracking
Shirazi, S., Sanderson, C., McCool, C., & Harandi, M. T. (2015). Bags of Affine Subspaces for Robust Object Tracking. In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE. http://doi.org/10.1109/DICTA.2015.7371239
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The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results
Felsberg, M., Berg, A., Häger, G., Ahlberg, J., Kristan, M., Matas, J., … Hong, Z. (2016). The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results. In 15th IEEE International Conference on Computer Vision Workshops (pp. 639–651). Santiago, Chile: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/ICCVW.2015.86
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Non-Invasive Performance Measurement in Combat Sports
Behendi, S. K., Morgan, S., & Fookes, C. B. (2016). Non-Invasive Performance Measurement in Combat Sports. In Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS) (pp. 3–10). Springer, Cham. http://doi.org/10.1007/978-3-319-24560-7_1
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Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
Wang, P., Shen, C., van den Hengel, A., & Torr, P. H. S. (2015). Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference. International Journal of Computer Vision, 117(3), 269–289. http://doi.org/10.1007/s11263-015-0865-2
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Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
*Zhang, F., Leitner, J., Milford, M., Upcroft, B., & Corke, P. (2015). Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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ORB Feature Extraction and Matching in Hardware
*Weberruss, J., Kleeman, L., & Drummond, T. (2015). ORB Feature Extraction and Matching in Hardware. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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Self-Calibration in Visual Sensor Networks Equipped with RGB-D Cameras
Wang, X., Sekercioglu, Y. A., & Drummond, T. (2015). Self-calibration in visual sensor networks equipped with RGB-D cameras. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015-August, 2289–2293. https://doi.org/10.1109/ICASSP.2015.7178379
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Efficient SDP Inference for Fully-connected CRFs Based on Low-rank Decomposition
Wang, P., Shen, C., & Van Den Hengel, A. (2015). Efficient SDP inference for fully-connected CRFs based on low-rank decomposition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 3222–3231. https://doi.org/10.1109/CVPR.2015.7298942
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On the Performance of ConvNet Features for Place Recognition
Sünderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., & Milford, M. (2015). On the performance of ConvNet features for place recognition. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 4297–4304. https://doi.org/10.1109/IROS.2015.7353986
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SLAM – Quo Vadis? In Support of Object Oriented and Semantic SLAM
*Sünderhauf, N., Dayoub, F., McMahon, S., Eich, M., Upcroft, B., & Milford, M. (2015). SLAM–Quo Vadis? In Support of Object Oriented and Semantic SLAM. Paper presented at the Robotics: Science and Systems (RSS) 2015, Rome, Italy.
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A Fast Method For Computing Principal Curvatures From Range Images
*Spek, A., & Drummond, T. (2015). A Fast Method For Computing Principal Curvatures From Range Images. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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Multimodal Deep Autoencoders for Control of a Mobile Robot
*Sergeant, J., Sünderhauf, N., Milford, M., & Upcroft, B. (2015). Multimodal Deep Autoencoders for Control of a Mobile Robot. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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Trajectory Alignment and Evaluation in SLAM: Horn’s Method vs Alignment on the Manifold
*Salas, M., Latif, Y., Reid, I. D., & Montiel, J. (2015). Trajectory Alignment and Evaluation in SLAM: Horn’s Method vs Alignment on the Manifold. Paper presented at the Robotics: Science and Systems (RSS) 2015.
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Enhancing Human Action Recognition with Region Proposals
*Rezazadegan, F., Shirazi, S., Sunderhauf, N., Milford, M., & Upcroft, B. (2015). Enhancing human action recognition with region proposals. Paper presented at the Australasian Conference on Robotics and Automation (ACRA2015), Australian National University, Canberra.
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The Effect of Different Parameterisations in Incremental Structure from Motion
Polok, L., Lui, V. W. H., Ila, V., Drummond, T. W., & Mahony, R. E. (2015). The effect of different parameterisations in incremental structure from motion (pp. 52–60). Australian Robotics and Automation Association (ARAA). https://research.monash.edu/en/publications/the-effect-of-different-parameterisations-in-incremental-structur
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Hierarchical Higher-order Regression Forest Fields: An Application to 3D Indoor Scene Labelling
*Pham, T. T., Reid, I., Latif, Y., & Gould, S. (2015). Hierarchical Higher-order Regression Forest Fields: An Application to 3D Indoor Scene Labelling. Paper presented at the International Conference on Computer Vision (ICCV) 2015.
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Automatic Image Scaling for Place Recognition in Changing Environments
Pepperell, E., Corke, P. I., & Milford, M. J. (2015). Automatic image scaling for place recognition in changing environments. Proceedings - IEEE International Conference on Robotics and Automation, 2015-June(June), 1118–1124. https://doi.org/10.1109/ICRA.2015.7139316
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Effective Semantic Pixel labelling with Convolutional Networks and Conditional Random Fields
Paisitkriangkrai, S., Sherrah, J., Janney, P., & Van-Den Hengel, A. (2015). Effective semantic pixel labelling with convolutional networks and Conditional Random Fields. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2015-October, 36–43. https://doi.org/10.1109/CVPRW.2015.7301381
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Monocular Image Space Tracking on a Computationally Limited MAV
Ok, K., Gamage, D., Drummond, T., Dellaert, F., & Roy, N. (2015). Monocular image space tracking on a computationally limited MAV. Proceedings - IEEE International Conference on Robotics and Automation, 2015-June(June), 6415–6422. https://doi.org/10.1109/ICRA.2015.7140100
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Sequence searching with deep-learnt depth for condition-and viewpoint-invariant route-based place recognition
Milford, M., Lowry, S., Sunderhauf, N., Shirazi, S., Pepperell, E., Upcroft, B., Shen, C., Lin, G., Liu, F., Cadena, C., & Reid, I. (2015). Sequence searching with deep-learnt depth for condition-and viewpoint-invariant route-based place recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2015-October, 18–25. https://doi.org/10.1109/CVPRW.2015.7301395
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TripNet: Detecting Trip Hazards on Construction Sites
*McMahon, S., Sünderhauf, N., Milford, M., & Upcroft, B. (2015). TripNet:Detecting Trip Hazards on Construction Sites. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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A Shallow Water AUV for Benthic and Water Column Observations
Marouchos, A., Muir, B., Babcock, R., & Dunbabin, M. (2015, September 17). A shallow water AUV for benthic and water column observations. MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World. https://doi.org/10.1109/OCEANS-Genova.2015.7271362
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A Linear Least-Squares Solution to Elastic Shape-from-Template
Malti, A., Bartoli, A., & Hartley, R. (2015). A linear least-squares solution to elastic Shape-from-Template. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 1629–1637. https://doi.org/10.1109/CVPR.2015.7298771
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Automating Marine Mammal Detection in Aerial Images Captured During Wildlife Surveys: a Deep Learning Approach
Maire F., Alvarez L.M., Hodgson A. (2015) Automating Marine Mammal Detection in Aerial Images Captured During Wildlife Surveys: A Deep Learning Approach. In: Pfahringer B., Renz J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science, vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_33
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Fast Inverse Compositional Image Alignment with Missing Data and Re-weighting
*Lui, V., Gamage, D., & Drummond, T. (2015). Fast Inverse Compositional Image Alignment with Missing Data and Re-weighting. Paper presented at the British Machine Vision Conference (BMVC) 2015, Swansea, UK.
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Image Based Optimisation without Global Consistency for Constant Time Monocular Visual SLAM
Lui, V., & Drummond, T. (2015). Image based optimisation without global consistency for constant time monocular visual SLAM. Proceedings - IEEE International Conference on Robotics and Automation, 2015-June(June), 5799–5806. https://doi.org/10.1109/ICRA.2015.7140011
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Deeply Learning the Messages in Message Passing Inference
*Lin, G., Shen, C., Reid, I., & Hengel, A. v. d. (2015). Deeply Learning the Messages in Message Passing Inference. Paper presented at the Neural Information Processing Systems (NIPS), 2015, Montreal, Canada.
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The use of Deep Learning Features in a Hierarchical Classifier Learned with the Minimization of a Non-Greedy Loss Function that Delays Gratification
Liao, Z., & Carneiro, G. (2015). The use of deep learning features in a hierarchical classifier learned with the minimization of a non-greedy loss function that delays gratification. Proceedings - International Conference on Image Processing, ICIP, 2015-December, 4540–4544. https://doi.org/10.1109/ICIP.2015.7351666
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Guiding the Long-Short Term Memory model for Image Caption Generation
*Jia, X., Gavves, S., Fernando, B., & Tuytelaars, T. (2015). Guided long-short term memory for image caption generation. Paper presented at the International Conference on Computer Vision (ICCV) 2015.
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Online Place Recognition Calibration for Out-of-the-Box SLAM
Jacobson, A., Chen, Z., & Milford, M. (2015). Online place recognition calibration for out-of-the-box SLAM. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 1357–1364. https://doi.org/10.1109/IROS.2015.7353544
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Five-Dimensional (5-D) Depth-Velocity Filtering for Enhancing Moving Objects in Light Field Videos
Edussooriya, C. U. S., Dansereau, D. G., Bruton, L. T., & Agathoklis, P. (2015). Five-Dimensional Depth-Velocity Filtering for Enhancing Moving Objects in Light Field Videos. IEEE Transactions on Signal Processing, 63(8), 2151–2163. https://doi.org/10.1109/TSP.2015.2408559
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Multi-Scale Place Recognition with Multi-Scale Sensing
*Jacobson, A., Chen, Z., Rallabandi, V. R., & Milford, M. (2015). Multi-Scale Place Recognition with Multi-Scale Sensing. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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Fast Covariance Recovery in Incremental Nonlinear Least Square Solvers
Ila, V., Polok, L., Solony, M., Smrz, P., & Zemcik, P. (2015). Fast covariance recovery in incremental nonlinear least square solvers. Proceedings - IEEE International Conference on Robotics and Automation, 2015-June(June), 4636–4643. https://doi.org/10.1109/ICRA.2015.7139841
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Evaluation of Features for Leaf Classification in Challenging Conditions
Hall, D., McCool, C., Dayoub, F., Sünderhauf, N., & Upcroft, B. (2015). Evaluation of features for leaf classification in challenging conditions. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 797–804. https://doi.org/10.1109/WACV.2015.111
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Subset Feature Learning for Fine-Grained Category Classification
Ge, Z., McCool, C., Sanderson, C., & Corke, P. (2015). Subset feature learning for fine-grained category classification. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2015-October, 46–52. https://doi.org/10.1109/CVPRW.2015.7301271
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Reduced Dimensionality Extended Kalman Filter for SLAM in a Relative Formulation
Gamage, D., & Drummond, T. (2015). Reduced dimensionality extended Kalman Filter for SLAM in a relative formulation. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 1365–1372. https://doi.org/10.1109/IROS.2015.7353545
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Modeling Video Evolution For Action Recognition
Fernando, B., Gavves, E., José Oramas, M., Ghodrati, A., & Tuytelaars, T. (2015). Modeling video evolution for action recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 5378–5387. https://doi.org/10.1109/CVPR.2015.7299176
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Learning to Rank Based on Subsequences
*Fernando, B., Gavves, E., Muselet, D., & Tuytelaars, T. (2015). Learning to rank based on subsequences. Paper presented at the International Conference on Computer Vision (ICCV) 2015.
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Material Classification on Symmetric Positive Definite Manifolds
Faraki, M., Harandi, M. T., & Porikli, F. (2015). Material classification on symmetric positive definite manifolds. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 749–756. https://doi.org/10.1109/WACV.2015.105
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Robotic Detection and Tracking of Crown-of-Thorns Starfish
Dayoub, F., Dunbabin, M., & Corke, P. (2015). Robotic detection and tracking of Crown-of-Thorns starfish. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 1921–1928. https://doi.org/10.1109/IROS.2015.7353629
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Closed-Form Change Detection from Moving Light Field Cameras
*Dansereau, D. G., Williams, S. B., & Corke, P. I. (2015). Closed-Form Change Detection from Moving Light Field Cameras. Paper presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015.
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Coverage-Based Next Best View Selection
Cunningham-Nelson, S., Moghadam, P., Roberts, J., & Elfes, A. (2015). Coverage-based next best view selection. Australasian Conference on Robotics and Automation, ACRA.
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Distance Metric Learning for Feature-Agnostic Place Recognition
Chen, Z., Lowry, S., Jacobson, A., Ge, Z., & Milford, M. (2015). Distance metric learning for feature-agnostic place recognition. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 2556–2563. https://doi.org/10.1109/IROS.2015.7353725
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Multi-class Semantic Video Segmentation with Exemplar-based Object Reasoning
Liu, B., He, X., & Gould, S. (2015). Multi-class semantic video segmentation with exemplar-based object reasoning. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 1014–1021. https://doi.org/10.1109/WACV.2015.140
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Iteratively Reweighted Graph Cut for Multi-label MRFs with Non-convex Priors
Ajanthan, T., Hartley, R., Salzmann, M., & Li, H. (2015). Iteratively reweighted graph cut for multi-label MRFs with non-convex priors. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 5144–5152. https://doi.org/10.1109/CVPR.2015.7299150
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LQ-Bundle Adjustment
Aftab, K., & Hartley, R. (2015). LQ-bundle adjustment. Proceedings - International Conference on Image Processing, ICIP, 2015-December, 1275–1279. https://doi.org/10.1109/ICIP.2015.7351005
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Convergence of Iteratively Re-weighted Least Squares to Robust M-estimators
Aftab, K., & Hartley, R. (2015). Convergence of iteratively re-weighted least squares to robust M-estimators. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 480–487. https://doi.org/10.1109/WACV.2015.70
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The Effect of Osteoimmunomodulation on the Osteogenic Effects of Cobalt Incorporated β-Tricalcium Phosphate
Chen, Z., Yuen, J., Crawford, R., Chang, J., Wu, C., & Xiao, Y. (2015). The effect of osteoimmunomodulation on the osteogenic effects of cobalt incorporated β-tricalcium phosphate. Biomaterials, 61, 126–138. https://doi.org/10.1016/j.biomaterials.2015.04.044
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An Automated Emergency Landing System for Fixed-Wing Aircraft: Planning and Control
Warren, M., Mejias, L., Kok, J., Yang, X., Gonzalez, F., & Upcroft, B. (2015). An Automated Emergency Landing System for Fixed-Wing Aircraft: Planning and Control. Journal of Field Robotics, 32(8), 1114–1140. https://doi.org/10.1002/rob.21641
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A Hybrid Loss for Multiclass and Structured Prediction
Shi, Q., Reid, M., Caetano, T., Van Den Hengel, A., & Wang, Z. (2015). A hybrid loss for multiclass and structured prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(1), 2–12. https://doi.org/10.1109/TPAMI.2014.2306414
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Outdoor Flight Testing of a Pole Inspection UAV Incorporating High-Speed Vision
Sa I., Hrabar S., Corke P. (2015) Outdoor Flight Testing of a Pole Inspection UAV Incorporating High-speed Vision. In: Mejias L., Corke P., Roberts J. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-319-07488-7_8
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An Evaluation of Crowd Counting Methods, Features and Regression Models
Ryan, D., Denman, S., Sridharan, S., & Fookes, C. (2015). An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding, 130, 1–17. https://doi.org/10.1016/j.cviu.2014.07.008
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Mirror Surface Reconstruction from a Single Image
Liu, M., Hartley, R., & Salzmann, M. (2015). Mirror surface reconstruction from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(4), 760–773. https://doi.org/10.1109/TPAMI.2014.2353622
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CRF Learning with CNN Features for Image Segmentation
Liu, F., Lin, G., & Shen, C. (2015). CRF learning with CNN features for image segmentation. Pattern Recognition, 48(10), 2983–2992. https://doi.org/10.1016/j.patcog.2015.04.019
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An Efficient and Robust System for Multi-Person Event Detection in Real World Indoor Surveillance Scenes
Xu, J., Denman, S., Sridharan, S., & Fookes, C. (2015). An efficient and robust system for multiperson event detection in real-world indoor surveillance scenes. IEEE Transactions on Circuits and Systems for Video Technology, 25(6), 1063–1076. https://doi.org/10.1109/TCSVT.2014.2367352
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Autonomous Multi-Sensor Calibration and Closed Loop Fusion for SLAM
Jacobson, A., Chen, Z., & Milford, M. (2015). Autonomous Multisensor Calibration and Closed-loop Fusion for SLAM. Journal of Field Robotics, 32(1), 85–122. https://doi.org/10.1002/rob.21500
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Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems
Li, H., Shen, C., Van Den Hengel, A., & Shi, Q. (2015). Worst case linear discriminant analysis as scalable semidefinite feasibility problems. IEEE Transactions on Image Processing, 24(8), 2382–2392. https://doi.org/10.1109/TIP.2015.2401511
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Joint Cross-Domain Classification and Subspace Learning for Unsupervised Adaptation
Fernando, B., Tommasi, T., & Tuytelaars, T. (2015). Joint cross-domain classification and subspace learning for unsupervised adaptation. Pattern Recognition Letters, 65, 60–66. https://doi.org/10.1016/j.patrec.2015.07.009
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Linear Volumetric Focus for Light Field Cameras
*Dansereau, D. G., Pizarro, O., & Williams, S. B. (2015). Linear Volumetric Focus for Light Field Cameras. ACM Transactions on Graphics (TOG), 34(2), 15.
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Bio-inspired homogeneous multi-scale place recognition
Chen, Z., Lowry, S., Jacobson, A., Hasselmo, M. E., & Milford, M. (2015). Bio-inspired homogeneous multi-scale place recognition. Neural Networks, 72, 48–61. https://doi.org/10.1016/j.neunet.2015.10.002
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A Vision Based Ensemble Approach to Velocity Estimation for Miniature Rotorcraft
Andersh, J., Cherian, A., Mettler, B., & Papanikolopoulos, N. (2015). A vision based ensemble approach to velocity estimation for miniature rotorcraft. Autonomous Robots, 39(2), 123–138. https://doi.org/10.1007/s10514-015-9430-7
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Generalized Weiszfeld Algorithms for Lq Optimization
Aftab, K., Hartley, R., & Trumpf, J. (2015). Generalized weiszfeld algorithms for Lq optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(4), 728–745. https://doi.org/10.1109/TPAMI.2014.2353625
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A Bottom-Up Integration of Vision and Actions To Create Cognitive Humanoids
Leitner, J. (2015). A Bottom-Up Integration of Vision and Actions To Create Cognitive Humanoids. In Cognitive Robotics (pp. 191–214). CRC Press. https://doi.org/10.1201/b19171-18
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Lie-Struck: Affine Tracking on Lie Groups using Structured SVM
Zhu, G., Porikli, F., Ming, Y., & Li, H. (2015). Lie-struck: Affine tracking on lie groups using structured SVM. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 63–70. https://doi.org/10.1109/WACV.2015.16
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High Breakdown Bundle Adjustment
Eriksson, A., Isaksson, M., & Chin, T. J. (2015). High breakdown bundle adjustment. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 310–317. https://doi.org/10.1109/WACV.2015.48
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Multi-Target Tracking with Time-Varying Clutter Rate and Detection Profile: Application to Time-lapse Cell Microscopy Sequences
Rezatofighi, S. H., Gould, S., Vo, B. T., Vo, B. N., Mele, K., & Hartley, R. (2015). Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences. IEEE Transactions on Medical Imaging, 34(6), 1336–1348. https://doi.org/10.1109/TMI.2015.2390647
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An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells
Lu, Z., Carneiro, G., & Bradley, A. P. (2015). An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Transactions on Image Processing, 24(4), 1261–1272. https://doi.org/10.1109/TIP.2015.2389619
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Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
Jayasumana, S., Hartley, R., Salzmann, M., Li, H., & Harandi, M. (2015). Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(12), 2464–2477. https://doi.org/10.1109/TPAMI.2015.2414422
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A Method for Extending Planar Axis-Symmetric Parallel Manipulators to Spatial Mechanisms
Isaksson, M., Eriksson, A., Watson, M., Brogårdh, T., & Nahavandi, S. (2015). A method for extending planar axis-symmetric parallel manipulators to spatial mechanisms. Mechanism and Machine Theory, 83(83), 1–13. https://doi.org/10.1016/j.mechmachtheory.2014.08.014
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Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
Harandi, M., Hartley, R., Shen, C., Lovell, B., & Sanderson, C. (2015). Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds. International Journal of Computer Vision, 114(2–3), 113–136. https://doi.org/10.1007/s11263-015-0833-x
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Learning Weighted Lower Linear Envelope Potentials in Binary Markov Random Fields
Gould, S. (2015). Learning Weighted Lower Linear Envelope Potentials in Binary Markov Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(7), 1336–1346. https://doi.org/10.1109/TPAMI.2014.2366760
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Book Chapters
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A Bottom-Up Integration of Vision and Actions To Create Cognitive Humanoids
Leitner, J. (2015). A Bottom-Up Integration of Vision and Actions To Create Cognitive Humanoids. In Cognitive Robotics (pp. 191–214). CRC Press. https://doi.org/10.1201/b19171-18
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Journal Articles
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12th Asian conference on computer vision
Reid, I. (2016). 12th Asian conference on computer vision. In Computer Vision and Image Understanding (Vol. 146, p. 51). Academic Press Inc. https://doi.org/10.1016/j.cviu.2016.03.016
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Superpixel-based appearance change prediction for long-term navigation across seasons
Neubert, P., Sünderhauf, N., & Protzel, P. (2015). Superpixel-based appearance change prediction for long-term navigation across seasons. Robotics and Autonomous Systems, 69(1), 15–27. https://doi.org/10.1016/j.robot.2014.08.005
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Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
Wang, P., Shen, C., van den Hengel, A., & Torr, P. H. S. (2015). Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference. International Journal of Computer Vision, 117(3), 269–289. http://doi.org/10.1007/s11263-015-0865-2
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Five-Dimensional (5-D) Depth-Velocity Filtering for Enhancing Moving Objects in Light Field Videos
Edussooriya, C. U. S., Dansereau, D. G., Bruton, L. T., & Agathoklis, P. (2015). Five-Dimensional Depth-Velocity Filtering for Enhancing Moving Objects in Light Field Videos. IEEE Transactions on Signal Processing, 63(8), 2151–2163. https://doi.org/10.1109/TSP.2015.2408559
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The Effect of Osteoimmunomodulation on the Osteogenic Effects of Cobalt Incorporated β-Tricalcium Phosphate
Chen, Z., Yuen, J., Crawford, R., Chang, J., Wu, C., & Xiao, Y. (2015). The effect of osteoimmunomodulation on the osteogenic effects of cobalt incorporated β-tricalcium phosphate. Biomaterials, 61, 126–138. https://doi.org/10.1016/j.biomaterials.2015.04.044
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An Automated Emergency Landing System for Fixed-Wing Aircraft: Planning and Control
Warren, M., Mejias, L., Kok, J., Yang, X., Gonzalez, F., & Upcroft, B. (2015). An Automated Emergency Landing System for Fixed-Wing Aircraft: Planning and Control. Journal of Field Robotics, 32(8), 1114–1140. https://doi.org/10.1002/rob.21641
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A Hybrid Loss for Multiclass and Structured Prediction
Shi, Q., Reid, M., Caetano, T., Van Den Hengel, A., & Wang, Z. (2015). A hybrid loss for multiclass and structured prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(1), 2–12. https://doi.org/10.1109/TPAMI.2014.2306414
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An Evaluation of Crowd Counting Methods, Features and Regression Models
Ryan, D., Denman, S., Sridharan, S., & Fookes, C. (2015). An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding, 130, 1–17. https://doi.org/10.1016/j.cviu.2014.07.008
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Mirror Surface Reconstruction from a Single Image
Liu, M., Hartley, R., & Salzmann, M. (2015). Mirror surface reconstruction from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(4), 760–773. https://doi.org/10.1109/TPAMI.2014.2353622
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CRF Learning with CNN Features for Image Segmentation
Liu, F., Lin, G., & Shen, C. (2015). CRF learning with CNN features for image segmentation. Pattern Recognition, 48(10), 2983–2992. https://doi.org/10.1016/j.patcog.2015.04.019
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An Efficient and Robust System for Multi-Person Event Detection in Real World Indoor Surveillance Scenes
Xu, J., Denman, S., Sridharan, S., & Fookes, C. (2015). An efficient and robust system for multiperson event detection in real-world indoor surveillance scenes. IEEE Transactions on Circuits and Systems for Video Technology, 25(6), 1063–1076. https://doi.org/10.1109/TCSVT.2014.2367352
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Autonomous Multi-Sensor Calibration and Closed Loop Fusion for SLAM
Jacobson, A., Chen, Z., & Milford, M. (2015). Autonomous Multisensor Calibration and Closed-loop Fusion for SLAM. Journal of Field Robotics, 32(1), 85–122. https://doi.org/10.1002/rob.21500
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Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems
Li, H., Shen, C., Van Den Hengel, A., & Shi, Q. (2015). Worst case linear discriminant analysis as scalable semidefinite feasibility problems. IEEE Transactions on Image Processing, 24(8), 2382–2392. https://doi.org/10.1109/TIP.2015.2401511
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Joint Cross-Domain Classification and Subspace Learning for Unsupervised Adaptation
Fernando, B., Tommasi, T., & Tuytelaars, T. (2015). Joint cross-domain classification and subspace learning for unsupervised adaptation. Pattern Recognition Letters, 65, 60–66. https://doi.org/10.1016/j.patrec.2015.07.009
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Linear Volumetric Focus for Light Field Cameras
*Dansereau, D. G., Pizarro, O., & Williams, S. B. (2015). Linear Volumetric Focus for Light Field Cameras. ACM Transactions on Graphics (TOG), 34(2), 15.
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Bio-inspired homogeneous multi-scale place recognition
Chen, Z., Lowry, S., Jacobson, A., Hasselmo, M. E., & Milford, M. (2015). Bio-inspired homogeneous multi-scale place recognition. Neural Networks, 72, 48–61. https://doi.org/10.1016/j.neunet.2015.10.002
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A Vision Based Ensemble Approach to Velocity Estimation for Miniature Rotorcraft
Andersh, J., Cherian, A., Mettler, B., & Papanikolopoulos, N. (2015). A vision based ensemble approach to velocity estimation for miniature rotorcraft. Autonomous Robots, 39(2), 123–138. https://doi.org/10.1007/s10514-015-9430-7
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Generalized Weiszfeld Algorithms for Lq Optimization
Aftab, K., Hartley, R., & Trumpf, J. (2015). Generalized weiszfeld algorithms for Lq optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(4), 728–745. https://doi.org/10.1109/TPAMI.2014.2353625
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Multi-Target Tracking with Time-Varying Clutter Rate and Detection Profile: Application to Time-lapse Cell Microscopy Sequences
Rezatofighi, S. H., Gould, S., Vo, B. T., Vo, B. N., Mele, K., & Hartley, R. (2015). Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences. IEEE Transactions on Medical Imaging, 34(6), 1336–1348. https://doi.org/10.1109/TMI.2015.2390647
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An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells
Lu, Z., Carneiro, G., & Bradley, A. P. (2015). An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Transactions on Image Processing, 24(4), 1261–1272. https://doi.org/10.1109/TIP.2015.2389619
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Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
Jayasumana, S., Hartley, R., Salzmann, M., Li, H., & Harandi, M. (2015). Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(12), 2464–2477. https://doi.org/10.1109/TPAMI.2015.2414422
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A Method for Extending Planar Axis-Symmetric Parallel Manipulators to Spatial Mechanisms
Isaksson, M., Eriksson, A., Watson, M., Brogårdh, T., & Nahavandi, S. (2015). A method for extending planar axis-symmetric parallel manipulators to spatial mechanisms. Mechanism and Machine Theory, 83(83), 1–13. https://doi.org/10.1016/j.mechmachtheory.2014.08.014
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Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
Harandi, M., Hartley, R., Shen, C., Lovell, B., & Sanderson, C. (2015). Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds. International Journal of Computer Vision, 114(2–3), 113–136. https://doi.org/10.1007/s11263-015-0833-x
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Learning Weighted Lower Linear Envelope Potentials in Binary Markov Random Fields
Gould, S. (2015). Learning Weighted Lower Linear Envelope Potentials in Binary Markov Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(7), 1336–1346. https://doi.org/10.1109/TPAMI.2014.2366760
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Conference Papers
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Detecting rare events using Kullback–Leibler divergence: A weakly supervised approach
Xu, J., Denman, S., Fookes, C., & Sridharan, S. (2015). Detecting rare events using Kullback-Leibler divergence. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015-August, 1305–1309. https://doi.org/10.1109/ICASSP.2015.7178181
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A Revisit of Methods for Determining the Fundamental Matrix with Planes
Zhou, Y., Kneip, L., & Li, H. (2015). A Revisit of Methods for Determining the Fundamental Matrix with Planes. In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE. http://doi.org/10.1109/DICTA.2015.7371221
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Bags of Affine Subspaces for Robust Object Tracking
Shirazi, S., Sanderson, C., McCool, C., & Harandi, M. T. (2015). Bags of Affine Subspaces for Robust Object Tracking. In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE. http://doi.org/10.1109/DICTA.2015.7371239
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The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results
Felsberg, M., Berg, A., Häger, G., Ahlberg, J., Kristan, M., Matas, J., … Hong, Z. (2016). The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results. In 15th IEEE International Conference on Computer Vision Workshops (pp. 639–651). Santiago, Chile: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/ICCVW.2015.86
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Non-Invasive Performance Measurement in Combat Sports
Behendi, S. K., Morgan, S., & Fookes, C. B. (2016). Non-Invasive Performance Measurement in Combat Sports. In Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS) (pp. 3–10). Springer, Cham. http://doi.org/10.1007/978-3-319-24560-7_1
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Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
*Zhang, F., Leitner, J., Milford, M., Upcroft, B., & Corke, P. (2015). Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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ORB Feature Extraction and Matching in Hardware
*Weberruss, J., Kleeman, L., & Drummond, T. (2015). ORB Feature Extraction and Matching in Hardware. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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Self-Calibration in Visual Sensor Networks Equipped with RGB-D Cameras
Wang, X., Sekercioglu, Y. A., & Drummond, T. (2015). Self-calibration in visual sensor networks equipped with RGB-D cameras. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015-August, 2289–2293. https://doi.org/10.1109/ICASSP.2015.7178379
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Efficient SDP Inference for Fully-connected CRFs Based on Low-rank Decomposition
Wang, P., Shen, C., & Van Den Hengel, A. (2015). Efficient SDP inference for fully-connected CRFs based on low-rank decomposition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 3222–3231. https://doi.org/10.1109/CVPR.2015.7298942
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On the Performance of ConvNet Features for Place Recognition
Sünderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., & Milford, M. (2015). On the performance of ConvNet features for place recognition. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 4297–4304. https://doi.org/10.1109/IROS.2015.7353986
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SLAM – Quo Vadis? In Support of Object Oriented and Semantic SLAM
*Sünderhauf, N., Dayoub, F., McMahon, S., Eich, M., Upcroft, B., & Milford, M. (2015). SLAM–Quo Vadis? In Support of Object Oriented and Semantic SLAM. Paper presented at the Robotics: Science and Systems (RSS) 2015, Rome, Italy.
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A Fast Method For Computing Principal Curvatures From Range Images
*Spek, A., & Drummond, T. (2015). A Fast Method For Computing Principal Curvatures From Range Images. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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Multimodal Deep Autoencoders for Control of a Mobile Robot
*Sergeant, J., Sünderhauf, N., Milford, M., & Upcroft, B. (2015). Multimodal Deep Autoencoders for Control of a Mobile Robot. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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Trajectory Alignment and Evaluation in SLAM: Horn’s Method vs Alignment on the Manifold
*Salas, M., Latif, Y., Reid, I. D., & Montiel, J. (2015). Trajectory Alignment and Evaluation in SLAM: Horn’s Method vs Alignment on the Manifold. Paper presented at the Robotics: Science and Systems (RSS) 2015.
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Enhancing Human Action Recognition with Region Proposals
*Rezazadegan, F., Shirazi, S., Sunderhauf, N., Milford, M., & Upcroft, B. (2015). Enhancing human action recognition with region proposals. Paper presented at the Australasian Conference on Robotics and Automation (ACRA2015), Australian National University, Canberra.
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The Effect of Different Parameterisations in Incremental Structure from Motion
Polok, L., Lui, V. W. H., Ila, V., Drummond, T. W., & Mahony, R. E. (2015). The effect of different parameterisations in incremental structure from motion (pp. 52–60). Australian Robotics and Automation Association (ARAA). https://research.monash.edu/en/publications/the-effect-of-different-parameterisations-in-incremental-structur
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Hierarchical Higher-order Regression Forest Fields: An Application to 3D Indoor Scene Labelling
*Pham, T. T., Reid, I., Latif, Y., & Gould, S. (2015). Hierarchical Higher-order Regression Forest Fields: An Application to 3D Indoor Scene Labelling. Paper presented at the International Conference on Computer Vision (ICCV) 2015.
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Automatic Image Scaling for Place Recognition in Changing Environments
Pepperell, E., Corke, P. I., & Milford, M. J. (2015). Automatic image scaling for place recognition in changing environments. Proceedings - IEEE International Conference on Robotics and Automation, 2015-June(June), 1118–1124. https://doi.org/10.1109/ICRA.2015.7139316
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Effective Semantic Pixel labelling with Convolutional Networks and Conditional Random Fields
Paisitkriangkrai, S., Sherrah, J., Janney, P., & Van-Den Hengel, A. (2015). Effective semantic pixel labelling with convolutional networks and Conditional Random Fields. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2015-October, 36–43. https://doi.org/10.1109/CVPRW.2015.7301381
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Monocular Image Space Tracking on a Computationally Limited MAV
Ok, K., Gamage, D., Drummond, T., Dellaert, F., & Roy, N. (2015). Monocular image space tracking on a computationally limited MAV. Proceedings - IEEE International Conference on Robotics and Automation, 2015-June(June), 6415–6422. https://doi.org/10.1109/ICRA.2015.7140100
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Sequence searching with deep-learnt depth for condition-and viewpoint-invariant route-based place recognition
Milford, M., Lowry, S., Sunderhauf, N., Shirazi, S., Pepperell, E., Upcroft, B., Shen, C., Lin, G., Liu, F., Cadena, C., & Reid, I. (2015). Sequence searching with deep-learnt depth for condition-and viewpoint-invariant route-based place recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2015-October, 18–25. https://doi.org/10.1109/CVPRW.2015.7301395
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TripNet: Detecting Trip Hazards on Construction Sites
*McMahon, S., Sünderhauf, N., Milford, M., & Upcroft, B. (2015). TripNet:Detecting Trip Hazards on Construction Sites. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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A Shallow Water AUV for Benthic and Water Column Observations
Marouchos, A., Muir, B., Babcock, R., & Dunbabin, M. (2015, September 17). A shallow water AUV for benthic and water column observations. MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World. https://doi.org/10.1109/OCEANS-Genova.2015.7271362
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A Linear Least-Squares Solution to Elastic Shape-from-Template
Malti, A., Bartoli, A., & Hartley, R. (2015). A linear least-squares solution to elastic Shape-from-Template. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 1629–1637. https://doi.org/10.1109/CVPR.2015.7298771
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Automating Marine Mammal Detection in Aerial Images Captured During Wildlife Surveys: a Deep Learning Approach
Maire F., Alvarez L.M., Hodgson A. (2015) Automating Marine Mammal Detection in Aerial Images Captured During Wildlife Surveys: A Deep Learning Approach. In: Pfahringer B., Renz J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science, vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_33
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Fast Inverse Compositional Image Alignment with Missing Data and Re-weighting
*Lui, V., Gamage, D., & Drummond, T. (2015). Fast Inverse Compositional Image Alignment with Missing Data and Re-weighting. Paper presented at the British Machine Vision Conference (BMVC) 2015, Swansea, UK.
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Image Based Optimisation without Global Consistency for Constant Time Monocular Visual SLAM
Lui, V., & Drummond, T. (2015). Image based optimisation without global consistency for constant time monocular visual SLAM. Proceedings - IEEE International Conference on Robotics and Automation, 2015-June(June), 5799–5806. https://doi.org/10.1109/ICRA.2015.7140011
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Deeply Learning the Messages in Message Passing Inference
*Lin, G., Shen, C., Reid, I., & Hengel, A. v. d. (2015). Deeply Learning the Messages in Message Passing Inference. Paper presented at the Neural Information Processing Systems (NIPS), 2015, Montreal, Canada.
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The use of Deep Learning Features in a Hierarchical Classifier Learned with the Minimization of a Non-Greedy Loss Function that Delays Gratification
Liao, Z., & Carneiro, G. (2015). The use of deep learning features in a hierarchical classifier learned with the minimization of a non-greedy loss function that delays gratification. Proceedings - International Conference on Image Processing, ICIP, 2015-December, 4540–4544. https://doi.org/10.1109/ICIP.2015.7351666
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Guiding the Long-Short Term Memory model for Image Caption Generation
*Jia, X., Gavves, S., Fernando, B., & Tuytelaars, T. (2015). Guided long-short term memory for image caption generation. Paper presented at the International Conference on Computer Vision (ICCV) 2015.
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Online Place Recognition Calibration for Out-of-the-Box SLAM
Jacobson, A., Chen, Z., & Milford, M. (2015). Online place recognition calibration for out-of-the-box SLAM. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 1357–1364. https://doi.org/10.1109/IROS.2015.7353544
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Multi-Scale Place Recognition with Multi-Scale Sensing
*Jacobson, A., Chen, Z., Rallabandi, V. R., & Milford, M. (2015). Multi-Scale Place Recognition with Multi-Scale Sensing. Paper presented at the Australasian Conference on Robotics and Automation (ACRA) 2015.
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Fast Covariance Recovery in Incremental Nonlinear Least Square Solvers
Ila, V., Polok, L., Solony, M., Smrz, P., & Zemcik, P. (2015). Fast covariance recovery in incremental nonlinear least square solvers. Proceedings - IEEE International Conference on Robotics and Automation, 2015-June(June), 4636–4643. https://doi.org/10.1109/ICRA.2015.7139841
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Evaluation of Features for Leaf Classification in Challenging Conditions
Hall, D., McCool, C., Dayoub, F., Sünderhauf, N., & Upcroft, B. (2015). Evaluation of features for leaf classification in challenging conditions. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 797–804. https://doi.org/10.1109/WACV.2015.111
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Subset Feature Learning for Fine-Grained Category Classification
Ge, Z., McCool, C., Sanderson, C., & Corke, P. (2015). Subset feature learning for fine-grained category classification. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2015-October, 46–52. https://doi.org/10.1109/CVPRW.2015.7301271
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Reduced Dimensionality Extended Kalman Filter for SLAM in a Relative Formulation
Gamage, D., & Drummond, T. (2015). Reduced dimensionality extended Kalman Filter for SLAM in a relative formulation. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 1365–1372. https://doi.org/10.1109/IROS.2015.7353545
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Modeling Video Evolution For Action Recognition
Fernando, B., Gavves, E., José Oramas, M., Ghodrati, A., & Tuytelaars, T. (2015). Modeling video evolution for action recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 5378–5387. https://doi.org/10.1109/CVPR.2015.7299176
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Learning to Rank Based on Subsequences
*Fernando, B., Gavves, E., Muselet, D., & Tuytelaars, T. (2015). Learning to rank based on subsequences. Paper presented at the International Conference on Computer Vision (ICCV) 2015.
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Material Classification on Symmetric Positive Definite Manifolds
Faraki, M., Harandi, M. T., & Porikli, F. (2015). Material classification on symmetric positive definite manifolds. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 749–756. https://doi.org/10.1109/WACV.2015.105
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Robotic Detection and Tracking of Crown-of-Thorns Starfish
Dayoub, F., Dunbabin, M., & Corke, P. (2015). Robotic detection and tracking of Crown-of-Thorns starfish. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 1921–1928. https://doi.org/10.1109/IROS.2015.7353629
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Closed-Form Change Detection from Moving Light Field Cameras
*Dansereau, D. G., Williams, S. B., & Corke, P. I. (2015). Closed-Form Change Detection from Moving Light Field Cameras. Paper presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015.
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Coverage-Based Next Best View Selection
Cunningham-Nelson, S., Moghadam, P., Roberts, J., & Elfes, A. (2015). Coverage-based next best view selection. Australasian Conference on Robotics and Automation, ACRA.
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Distance Metric Learning for Feature-Agnostic Place Recognition
Chen, Z., Lowry, S., Jacobson, A., Ge, Z., & Milford, M. (2015). Distance metric learning for feature-agnostic place recognition. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 2556–2563. https://doi.org/10.1109/IROS.2015.7353725
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Multi-class Semantic Video Segmentation with Exemplar-based Object Reasoning
Liu, B., He, X., & Gould, S. (2015). Multi-class semantic video segmentation with exemplar-based object reasoning. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 1014–1021. https://doi.org/10.1109/WACV.2015.140
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Iteratively Reweighted Graph Cut for Multi-label MRFs with Non-convex Priors
Ajanthan, T., Hartley, R., Salzmann, M., & Li, H. (2015). Iteratively reweighted graph cut for multi-label MRFs with non-convex priors. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 5144–5152. https://doi.org/10.1109/CVPR.2015.7299150
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LQ-Bundle Adjustment
Aftab, K., & Hartley, R. (2015). LQ-bundle adjustment. Proceedings - International Conference on Image Processing, ICIP, 2015-December, 1275–1279. https://doi.org/10.1109/ICIP.2015.7351005
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Convergence of Iteratively Re-weighted Least Squares to Robust M-estimators
Aftab, K., & Hartley, R. (2015). Convergence of iteratively re-weighted least squares to robust M-estimators. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 480–487. https://doi.org/10.1109/WACV.2015.70
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Outdoor Flight Testing of a Pole Inspection UAV Incorporating High-Speed Vision
Sa I., Hrabar S., Corke P. (2015) Outdoor Flight Testing of a Pole Inspection UAV Incorporating High-speed Vision. In: Mejias L., Corke P., Roberts J. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-319-07488-7_8
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Lie-Struck: Affine Tracking on Lie Groups using Structured SVM
Zhu, G., Porikli, F., Ming, Y., & Li, H. (2015). Lie-struck: Affine tracking on lie groups using structured SVM. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 63–70. https://doi.org/10.1109/WACV.2015.16
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High Breakdown Bundle Adjustment
Eriksson, A., Isaksson, M., & Chin, T. J. (2015). High breakdown bundle adjustment. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 310–317. https://doi.org/10.1109/WACV.2015.48
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