2016 Publications

Scientific Publications

  • Quantifying Spatiotemporal Greenhouse Gas Emissions Using Autonomous Surface Vehicles

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

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  • Advances in Visual Computing

    Bebis, G., Boyle, R., Parvin, et al. (2016). Advances in Visual Computing. (Vol. 10072). Cham: Springer International Publishing. http://doi.org/10.1007/978-3-319-50835-1

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

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

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  • Computer Vision and Image Understanding (Vol. 146)

    Reid, I. (2016). 12th Asian conference on computer vision. Computer Vision and Image Understanding (Vol. 146).

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  • Robotics Research: The 16th International Symposium ISRR

    Inaba, M., & Corke, P. (2016). Robotics research: The 16th international symposium ISRR. In 16th International Symposium of Robotics Research, ISRR 2013 (Vol. 114). Singapore: Springer Verlag. http://doi.org/10.1007/978-3-319-28872-7

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  • Fast Training of Triplet-based Deep Binary Embedding Networks

    Zhuang, B., Lin, G., Shen, C., & Reid, I. (2016). Fast training of triplet-based deep binary embedding networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 5955–5964. https://doi.org/10.1109/CVPR.2016.641

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  • Robust Visual Tracking with Deep Convolutional Neural Network Based Object Proposals on PETS

    Zhu, G., Porikli, F., & Li, H. (2016). Robust Visual Tracking with Deep Convolutional Neural Network Based Object Proposals on PETS. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 (pp. 1265–1272). Las Vegas, Nevada: IEEE Computer Society. http://doi.org/10.1109/CVPRW.2016.160

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  • Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals

    Zhu, G., Porikli, F., & Li, H. (2016). Beyond local search: Tracking objects everywhere with instance-specific proposals. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 943–951. https://doi.org/10.1109/CVPR.2016.108

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  • Real-time Rotation Estimation for Dense Depth Sensors in Piece-wise Planar Environments

    Zhou, Y., Kneip, L., & Li, H. (2016). Real-time rotation estimation for dense depth sensors in piece-wise planar environments. IEEE International Conference on Intelligent Robots and Systems, 2016-November, 2271–2278. https://doi.org/10.1109/IROS.2016.7759355

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  • Less Is More: Towards Compact CNNs

    Zhou H., Alvarez J.M., Porikli F. (2016) Less Is More: Towards Compact CNNs. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9908. Springer, Cham. https://doi.org/10.1007/978-3-319-46493-0_40

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  • Cluster Sparsity Field for Hyperspectral Imagery Denoising

    Zhang L., Wei W., Zhang Y., Shen C., van den Hengel A., Shi Q. (2016) Cluster Sparsity Field for Hyperspectral Imagery Denoising. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9909. Springer, Cham. https://doi.org/10.1007/978-3-319-46454-1_38

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  • SLNSW-UTS: A Historical Image Dataset for Image Multi-Labeling and Retrieval

    Zhang, J., Zhang, J., Lu, J., Shen, C., Curr, K., Phua, R., Neville, R., & Edmonds, E. (2016). SLNSW-UTS: A Historical Image Dataset for Image Multi-Labeling and Retrieval. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–6). Gold Coast, Australia: IEEE. http://doi.org/10.1109/DICTA.2016.7797084

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  • Vertical Axis Detection for Sport Video Analytics

    Zeng, R., Lakemond, R., Denman, S., Sridharan, S., Fookes, C., & Morgan, S. (2016). Vertical Axis Detection for Sport Video Analytics. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE. http://doi.org/10.1109/DICTA.2016.7797093

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  • Ultra-Resolving Face Images by Discriminative Generative Networks

    Yu X., Porikli F. (2016) Ultra-Resolving Face Images by Discriminative Generative Networks. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9909. Springer, Cham. https://doi.org/10.1007/978-3-319-46454-1_20

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  • Riemannian Sparse Coding for Classification of PolSAR Images

    Yang, W., Zhong, N., Yang, X., & Cherian, A. (2016). Riemannian sparse coding for classification of PolSAR images. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5698–5701). Beijing, China: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/IGARSS.2016.7730488

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  • Robust Optical Flow Estimation of Double-Layer Images under Transparency or Reflection

    Yang, J., Li, H., Dai, Y., & Tan, R. T. (2016). Robust optical flow estimation of double-layer images under transparency or reflection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 1410–1419. https://doi.org/10.1109/CVPR.2016.157

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  • Superpixel-Based Two-View Deterministic Fitting for Multiple-Structure Data

    Xiao G., Wang H., Yan Y., Suter D. (2016) Superpixel-Based Two-View Deterministic Fitting for Multiple-Structure Data. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9910. Springer, Cham. https://doi.org/10.1007/978-3-319-46466-4_31

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  • Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources

    Wu, Q., Wang, P., Shen, C., Dick, A., & Van Den Hengel, A. (2016). Ask me anything: Free-form visual question answering based on knowledge from external sources. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 4622–4630. https://doi.org/10.1109/CVPR.2016.500

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  • What Value Do Explicit High Level Concepts Have in Vision to Language Problems?

    Wu, Q., Shen, C., Liu, L., Dick, A., & Van Den Hengel, A. (2016). What value do explicit high level concepts have in vision to language problems? Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 203–212. https://doi.org/10.1109/CVPR.2016.29

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  • Towards Hybrid Control of a Flexible Curvilinear Surgical Robot With Visual/Haptic Guidance

    Wu, L., Wu, K., & Ren, H. (2016). Towards hybrid control of a flexible curvilinear surgical robot with visual/haptic guidance. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 (pp. 501–507). Daejeon, Korea: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/IROS.2016.7759100

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  • Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering

    Wang, Y., Wenjie, Z., Wu, L., Lin, X., Fang, M., & Pan, S. (2016). Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering (pp. 2153–2159). Association for the Advancement of Artificial Intelligence (AAAI). https://research.monash.edu/en/publications/iterative-views-agreement-an-iterative-low-rank-based-structured-

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  • Collaborative Multi-Sensor Image Transmission and Data Fusion in Mobile Visual Sensor Networks Equipped with RGB-D Cameras

    Wang, X., Ahmet Sekercioglu, Y., Drummond, T., Natalizio, E., Fantoni, I., & Fremont, V. (2016). Collaborative multi-sensor image transmission and data fusion in mobile visual sensor networks equipped with RGB-D cameras. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 0, 1–8. https://doi.org/10.1109/MFI.2016.7849458

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  • UAV Based Target Finding and Tracking in GPS-Denied and Cluttered Environments

    Vanegas, F., Campbell, D., Eich, M., & Gonzalez, F. (2016). UAV based target finding and tracking in GPS-denied and cluttered environments. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 (pp. 2307–2313). Daejeon, South Korea: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/IROS.2016.7759360

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  • Find my office: Navigating real space from semantic descriptions

    Talbot, B., Lam, O., Schulz, R., Dayoub, F., Upcroft, B., & Wyeth, G. (2016). Find my office: Navigating real space from semantic descriptions. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 5782–5787. https://doi.org/10.1109/ICRA.2016.7487802

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  • Place Categorization and Semantic Mapping on a Mobile Robot

    Sunderhauf, N., Dayoub, F., McMahon, S., Talbot, B., Schulz, R., Corke, P., … Milford, M. (2016). Place categorization and semantic mapping on a mobile robot. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5729–5736). IEEE. http://doi.org/10.1109/ICRA.2016.7487796

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  • Learning Functional Argument Mappings for Hierarchical Tasks from Situation Specific Explanations

    Suddrey, G., Eich, M., Maire, F., & Roberts, J. (2016). Learning Functional Argument Mappings for Hierarchical Tasks from Situation Specific Explanations. In AI 2016: Advances in Artificial Intelligence (pp. 345–352). Springer, Cham. http://doi.org/10.1007/978-3-319-50127-7_30

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  • Towards Robotic Arthroscopy: “Instrument gap” Segmentation

    Strydom, M., Jaiprakash, A., Crawford, R., Peynot, T., & Roberts, J. (2016). Towards robotic arthroscopy: “Instrument gap” segmentation. Australasian Conference on Robotics and Automation, ACRA, 2016-December, 248–257.

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  • Skyline-based Localisation for Aggressively Manoeuvring Robots using UV sensors and Spherical Harmonics

    Stone, T., Differt, D., Milford, M., & Webb, B. (2016). Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5615–5622). Stockholm: IEEE. http://doi.org/10.1109/ICRA.2016.7487780

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  • High-Fidelity Simulation for Evaluating Robotic Vision Performance

    Skinner, J., Garg, S., Sunderhauf, N., Corke, P., Upcroft, B., & Milford, M. (2016). High-fidelity simulation for evaluating robotic vision performance. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Daejeon, Korea. http://doi.org/10.1109/IROS.2016.7759425

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  • Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

    Saleh F., Aliakbarian M.S., Salzmann M., Petersson L., Gould S., Alvarez J.M. (2016) Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9912. Springer, Cham. https://doi.org/10.1007/978-3-319-46484-8_25

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  • Joint Probabilistic Matching Using m-Best Solutions

    Rezatofighi, S. H., Milani, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2016). Joint probabilistic matching using m-best solutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 136–145. https://doi.org/10.1109/CVPR.2016.22

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  • Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression

    Qiao, R., Liu, L., Shen, C., & Hengel, A. Van Den. (2016). Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 2249–2257. https://doi.org/10.1109/CVPR.2016.247

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  • Design and fabrication of a disposable micro end effector for concentric tube robots

    Prasai, A. B., Jaiprakash, A., Pandey, A. K., Crawford, R., Roberts, J., & Wu, L. (2016). Design and fabrication of a disposable micro end effector for concentric tube robots. 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016. https://doi.org/10.1109/ICARCV.2016.7838560

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  • 3D Reconstruction Quality Analysis and Its Acceleration on GPU Clusters

    Polok, L., Ila, V., & Smrz, P. (2016). 3D reconstruction quality analysis and its acceleration on GPU clusters. In European Signal Processing Conference (EUSIPCO) (Vol. 2016–Novem, pp. 1108–1112). Budapest, Hungary. http://doi.org/10.1109/EUSIPCO.2016.7760420

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  • Efficient Point Process Inference for Large-scale Object Detection

    Pham, T. T., Rezatofighi, S. H., Reid, I., & Chin, T. J. (2016). Efficient Point Process Inference for Large-Scale Object Detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 2837–2845. https://doi.org/10.1109/CVPR.2016.310

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  • Geometrically Consistent Plane Extraction for Dense Indoor 3D Maps Segmentation

    Pham, T. T., Eich, M., Reid, I., & Wyeth, G. (2016). Geometrically consistent plane extraction for dense indoor 3D maps segmentation. IEEE International Conference on Intelligent Robots and Systems, 2016-November, 4199–4204. https://doi.org/10.1109/IROS.2016.7759618

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  • Deeper and Wider Fully Convolutional Network Coupled with Conditional Random Fields for Scene Labeling

    Nguyen, K., Fookes, C., & Sridharan, S. (2016). Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling. Proceedings - International Conference on Image Processing, ICIP, 2016-August, 1344–1348. https://doi.org/10.1109/ICIP.2016.7532577

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  • 3D Scanning System for Automatic High-Resolution Plant Phenotyping

    Nguyen, C. V., Fripp, J., Lovell, D. R., Furbank, R., Kuffner, P., Daily, H., & Sirault, X. (2016). 3D Scanning System for Automatic High-Resolution Plant Phenotyping. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–8). Gold Coast, Queensland: IEEE. http://doi.org/10.1109/DICTA.2016.7796984

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  • Non-Iterative, Fast SE(3) Path Smoothing

    Ng, Y., Jiang, B., Yu, C., & Li, H. (2016). Non-iterative, fast SE(3) path smoothing. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 (pp. 3172–3179). Daejeon, Korea: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/IROS.2016.7759490

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  • Latent Structural SVM with Marginal Probabilities for Weakly Labeled Structured Learning

    *Namin, S. R., Alvarez, J. M., Kneip, L., & Petersson, L. (2016). Latent structural SVM with marginal probabilities for weakly labeled structured learning. In 23rd IEEE International Conference on Image Processing, ICIP 2016 (pp. 3733–3737). Phoenix, United States: IEEE Computer Society.

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  • 2D Visual Place Recognition for Domestic Service Robots at Night

    Mount, J., & Milford, M. (2016). 2D visual place recognition for domestic service robots at night. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 4822–4829. https://doi.org/10.1109/ICRA.2016.7487686

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  • Visual Detection of Occluded Crop: for automated harvesting

    McCool, C., Sa, I., Dayoub, F., Lehnert, C., Perez, T., & Upcroft, B. (2016). Visual detection of occluded crop: For automated harvesting. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 2506–2512. https://doi.org/10.1109/ICRA.2016.7487405

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  • Underwater Image Descattering and Quality Assessment

    Lu, H., Li, Y., Xu, X., He, L., Li, Y., Dansereau, D., & Serikawa, S. (2016). Underwater image descattering and quality assessment. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 1998–2002). IEEE. http://doi.org/10.1109/ICIP.2016.7532708

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  • Learning Image Matching by Simply Watching Video

    Long G., Kneip L., Alvarez J.M., Li H., Zhang X., Yu Q. (2016) Learning Image Matching by Simply Watching Video. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9910. Springer, Cham. https://doi.org/10.1007/978-3-319-46466-4_26

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  • Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

    Lin, G., Shen, C., Hengel, A. Van Den, & Reid, I. (2016). Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 3194–3203. https://doi.org/10.1109/CVPR.2016.348

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  • Recent Advances in Camera Planning for Large Area Surveillance

    Liu, J., Sridharan, S., & Fookes, C. (2016). Recent Advances in Camera Planning for Large Area Surveillance. ACM Computing Surveys, 49(1), 1–37. http://doi.org/10.1145/2906148

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  • On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units

    Liao, Z., & Carneiro, G. (2016). On the importance of normalisation layers in deep learning with piecewise linear activation units. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1–8). IEEE. http://doi.org/10.1109/WACV.2016.7477624

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  • Design and Flight Testing of a Bio-Inspired Plume Tracking Algorithm for Unmanned Aerial Vehicles

    Letheren, B., Montes, G., Villa, T., & Gonzalez, F. (2016). Design and flight testing of a bio-inspired plume tracking algorithm for unmanned aerial vehicles. IEEE Aerospace Conference Proceedings, 2016-June. https://doi.org/10.1109/AERO.2016.7500614

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  • LunaRoo: Designing a Hopping Lunar Science Payload

    Leitner, J., Chamberlain, W., Dansereau, D. G., Dunbabin, M., Eich, M., Peynot, T., … Sunderhauf, N. (2016). LunaRoo: Designing a hopping lunar science payload. In 2016 IEEE Aerospace Conference (pp. 1–12). IEEE. http://doi.org/10.1109/AERO.2016.7500760

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  • Sweet Pepper Pose Detection and Grasping for Automated Crop Harvesting

    Lehnert, C., Sa, I., McCool, C., Upcroft, B., & Perez, T. (2016). Sweet pepper pose detection and grasping for automated crop harvesting. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 2428–2434. https://doi.org/10.1109/ICRA.2016.7487394

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  • Conformal Surface Alignment With Optimal Mobius Search

    Le, H., Chin, T. J., & Suter, D. (2016). Conformal Surface Alignment with Optimal Möbius Search. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 2507–2516. https://doi.org/10.1109/CVPR.2016.275

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  • Multi-body non-rigid structure-from-motion

    Kumar, S., Dai, Y., & Li, H. (2016). Multi-body non-rigid structure-from-motion. In Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016 (pp. 148–156). Stanford, United States: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/3DV.2016.23

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  • Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions

    Kumar, B. G. V., Carneiro, G., & Reid, I. (2016). Learning local image descriptors with deep siamese and triplet convolutional networks by minimizing global loss functions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 5385–5394. https://doi.org/10.1109/CVPR.2016.581

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  • Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons

    Koniusz P., Cherian A., Porikli F. (2016) Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9908. Springer, Cham. https://doi.org/10.1007/978-3-319-46493-0_3

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  • Sparse Coding for Third-order Super-symmetric Tensor Descriptors with Application to Texture Recognition

    Koniusz, P., & Cherian, A. (2016). Sparse coding for third-order super-symmetric tensor descriptors with application to texture recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 5395–5403. https://doi.org/10.1109/CVPR.2016.582

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  • The Generalized Relative Pose and Scale Problem: View-Graph Fusion via 2D-2D Registration

    Kneip, L., Sweeney, C., & Hartley, R. (2016). The generalized relative pose and scale problem: View-graph fusion via 2D-2D registration. In IEEE Winter Conference on Applications of Computer Vision, WACV 2016. Lake Placid, United States: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/WACV.2016.7477656

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  • Direct Semi-dense SLAM for Rolling Shutter Cameras

    Kim, J. H., Cadena, C., & Reid, I. (2016). Direct semi-dense SLAM for rolling shutter cameras. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 1308–1315. https://doi.org/10.1109/ICRA.2016.7487263

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  • Deep Convolutional Neural Networks for Human Embryonic Cell Counting

    Khan A., Gould S., Salzmann M. (2016) Deep Convolutional Neural Networks for Human Embryonic Cell Counting. In: Hua G., Jégou H. (eds) Computer Vision – ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science, vol 9913. Springer, Cham. https://doi.org/10.1007/978-3-319-46604-0_25

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  • Unmanned Aerial Surveillance System for Hazard Collision Avoidance in Autonomous Shipping

    Johansen, T. A., & Perez, T. (2016). Unmanned aerial surveillance system for hazard collision avoidance in autonomous shipping. In 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1056–1065). IEEE. http://doi.org/10.1109/ICUAS.2016.7502542

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  • Robust Multi-body Feature Tracker: A Segmentation-free Approach

    Ji, P., Li, H., Salzmann, M., & Zhong, Y. (2016). Robust Multi-Body Feature Tracker: A Segmentation-Free Approach. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 3843–3851. https://doi.org/10.1109/CVPR.2016.417

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  • Haptics-Aided Path Planning and Virtual Fixture Based Dynamic Kinesthetic Boundary for Bilateral Teleoperation of VTOL Aerial Robots

    Hou, X., Wang, X., & Mahony, R. (2016). Haptics-aided path planning and virtual fixture based dynamic kinesthetic boundary for bilateral teleoperation of VTOL aerial robots. Chinese Control Conference, CCC, 2016-August, 4705–4710. https://doi.org/10.1109/ChiCC.2016.7554082

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  • Adaptive spatial filtering for off-axis digital holographic microscopy based on region recognition approach with iterative thresholding

    He, X., Nguyen, C. V., Pratap, M., Zheng, Y., Wang, Y., Nisbet, D. R., Rug, M., Maier, A. G., & Lee, W. M. (2016). Adaptive spatial filtering for off-axis digital holographic microscopy based on region recognition approach with iterative thresholding. In M. R. Hutchinson & E. M. Goldys (Eds.), SPIE BioPhotonics Australasia (Vol. 10013, p. 1001329). SPIE. https://doi.org/10.1117/12.2242876

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  • FANNG: Fast Approximate Nearest Neighbour Graphs

    Harwood, B., & Drummond, T. (2016). FANNG: Fast approximate nearest neighbour graphs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 5713–5722. https://doi.org/10.1109/CVPR.2016.616

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  • Discovery of Facial Motions using Deep Machine Perception

    Ghasemi, A., Denman, S., Sridharan, S., & Fookes, C. (2016, May 23). Discovery of facial motions using deep machine perception. 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. https://doi.org/10.1109/WACV.2016.7477448

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  • Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification

    Ge, Z., McCool, C., Sanderson, C., Wang, P., Liu, L., Reid, I., & Corke, P. (2016). Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification. In Digital Image Computing: Techniques and Applications (DICTA). Gold Coast, Queensland. http://doi.org/10.1109/DICTA.2016.7797039

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  • Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks

    Ge, Z., Bewley, A., McCool, C., Corke, P., Upcroft, B., & Sanderson, C. (2016). Fine-grained classification via mixture of deep convolutional neural networks. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1–6). IEEE. http://doi.org/10.1109/WACV.2016.7477700

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  • Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

    Garg R., B.G. V.K., Carneiro G., Reid I. (2016) Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9912. Springer, Cham. https://doi.org/10.1007/978-3-319-46484-8_45

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  • Automated Plant and Leaf Separation: Application in 3D Meshes of Wheat Plants

    Frolov, K., Fripp, J., Nguyen, C. V., Furbank, R., Bull, G., Kuffner, P., … Sirault, X. (2016). Automated Plant and Leaf Separation: Application in 3D Meshes of Wheat Plants. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). Gold Coast, Queensland: IEEE. http://doi.org/10.1109/DICTA.2016.7797011

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  • Discriminative Hierarchical Rank Pooling for Activity Recognition

    Fernando, B., Anderson, P., Hutter, M., & Gould, S. (2016). Discriminative Hierarchical Rank Pooling for Activity Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 1924–1932. https://doi.org/10.1109/CVPR.2016.212

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  • A Consensus-Based Framework for Distributed Bundle Adjustment

    Eriksson, A., Bastian, J., Chin, T. J., & Isaksson, M. (2016). A Consensus-Based Framework for Distributed Bundle Adjustment. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 1754–1762. https://doi.org/10.1109/CVPR.2016.194

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  • Autonomous Greenhouse Gas Sampling Using Multiple Robotic Boats

    Dunbabin, M. (2016). Autonomous greenhouse gas sampling using multiple robotic boats. In 10th International Conference on Field and Service Robotics, FSR 2015 (Vol. 113, pp. 17–30). Toronto, Canada: Springer Verlag. http://doi.org/10.1007/978-3-319-27702-8_2

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  • Reliable Scale Estimation and Correction for Monocular Visual Odometry

    Dingfu Zhou, Dai, Y., & Hongdong Li. (2016). Reliable scale estimation and correction for monocular Visual Odometry. In 2016 IEEE Intelligent Vehicles Symposium (IV) (pp. 490–495). Gothenburg, Sweden: IEEE. http://doi.org/10.1109/IVS.2016.7535431

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  • MO-SLAM: Multi Object SLAM with Run-Time Object Discovery through Duplicates

    Dharmasiri, T., Lui, V., & Drummond, T. (2016). MO-SLAM: Multi object SLAM with run-time object discovery through duplicates - IEEE Xplore Document. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Daejeon, Korea. http://doi.org/10.1109/IROS.2016.7759203

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  • Output Regulation on the Special Euclidean Group SE(3)

    De Marco, S., Marconi, L., Hamel, T., & Mahony, R. (2016). Output regulation on the Special Euclidean Group SE(3). 2016 IEEE 55th Conference on Decision and Control, CDC 2016, 4734–4739. https://doi.org/10.1109/CDC.2016.7798991

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  • Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry

    Dai, Y., Li, H., & Kneip, L. (2016). Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 4132–4140. https://doi.org/10.1109/CVPR.2016.448

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  • Simultaneous Correspondences Estimation and Non-Rigid Structure Reconstruction

    Dai, Y., & Li, H. (2016). Simultaneous Correspondences Estimation and Non-Rigid Structure Reconstruction. In 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016. Gold Coast, Queensland: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/DICTA.2016.7797083

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  • Guaranteed Outlier Removal With Mixed Integer Linear Programs

    Chin, T. J., Kee, Y. H., Eriksson, A., & Neumann, F. (2016). Guaranteed outlier removal with mixed integer linear programs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 5858–5866. https://doi.org/10.1109/CVPR.2016.631

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  • A Distributed Robotic Vision Service

    Chamberlain, W., Leitner, J., Drummond, T., & Corke, P. (2016). A distributed robotic vision service. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 2494–2499. https://doi.org/10.1109/ICRA.2016.7487403

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  • Dynamic Image Networks for Action Recognition

    Bilen, H., Fernando, B., Gavves, E., Vedaldi, A., & Gould, S. (2016). Dynamic Image Networks for Action Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 3034–3042. https://doi.org/10.1109/CVPR.2016.331

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  • ALExTRAC: Affinity Learning by Exploring Temporal Reinforcement within Association Chains

    Bewley, A., Ott, L., Ramos, F., & Upcroft, B. (2016). Alextrac: Affinity learning by exploring temporal reinforcement within association chains. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2212–2218). Stockholm, Sweden: IEEE. http://doi.org/10.1109/ICRA.2016.7487371

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  • Simple Online and Realtime Tracking

    Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016). Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 3464–3468). IEEE. http://doi.org/10.1109/ICIP.2016.7533003

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  • SPICE: Semantic Propositional Image Caption Evaluation

    Anderson P., Fernando B., Johnson M., Gould S. (2016) SPICE: Semantic Propositional Image Caption Evaluation. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9909. Springer, Cham. https://doi.org/10.1007/978-3-319-46454-1_24

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  • Velocity Aided Attitude Estimation for Aerial Robotic Vehicles Using Latent Rotation Scaling

    Allibert, G., Mahony, R., & Bangura, M. (2016). Velocity aided attitude estimation for aerial robotic vehicles using latent rotation scaling. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 1538–1543. https://doi.org/10.1109/ICRA.2016.7487291

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  • Complex Event Detection using Joint Max Margin and Semantic Features

    Abbasnejad, I., Sridharan, S., Denman, S., Fookes, C., & Lucey, S. (2016, December 22). Complex Event Detection Using Joint Max Margin and Semantic Features. 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016. https://doi.org/10.1109/DICTA.2016.7797023

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  • Dictionary Learning for Promoting Structured Sparsity in Hyprspectral Compressive Sensing

    Zhang, L., Wei, W., Zhang, Y., Shen, C., Van Den Hengel, A., & Shi, Q. (2016). Dictionary Learning for Promoting Structured Sparsity in Hyperspectral Compressive Sensing. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7223–7235. https://doi.org/10.1109/TGRS.2016.2598577

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  • Unsupervised Feature Learning for Dense Correspondences Across Scenes

    Zhang, C., Shen, C., & Shen, T. (2016). Unsupervised Feature Learning for Dense Correspondences Across Scenes. International Journal of Computer Vision, 116(1), 90–107. https://doi.org/10.1007/s11263-015-0829-6

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  • Development of a Multi-Channel Concentric Tube Robotic System With Active Vision for Transnasal Nasopharyngeal Carcinoma Procedures

    Yu, H., Wu, L., Wu, K., & Ren, H. (2016). Development of a Multi-Channel Concentric Tube Robotic System with Active Vision for Transnasal Nasopharyngeal Carcinoma Procedures. IEEE Robotics and Automation Letters, 1(2), 1172–1178. https://doi.org/10.1109/LRA.2016.2530794

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  • Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration

    Yang, J., Li, H., Campbell, D., & Jia, Y. (2016). Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2241–2254. http://doi.org/10.1109/TPAMI.2015.2513405

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  • Detecting Rare Events Using Kullback–Leibler Divergence: A Weakly Supervised Approach

    Xu, J., Denman, S., Fookes, C., & Sridharan, S. (2016). Detecting rare events using Kullback–Leibler divergence: A weakly supervised approach. Expert Systems with Applications, 54, 13–28. http://doi.org/10.1016/j.eswa.2016.01.035

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  • Hypergraph Modelling for Geometric Model Fitting

    Xiao, G., Wang, H., Lai, T., & Suter, D. (2016). Hypergraph modelling for geometric model fitting. Pattern Recognition, 60, 748–760. https://doi.org/10.1016/j.patcog.2016.06.026

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  • Simultaneous Hand–Eye, Tool–Flange, and Robot–Robot Calibration for Comanipulation by Solving the AXB = YCZ Problem

    Wu, L., Wang, J., Qi, L., Wu, K., Ren, H., & Meng, M. Q. H. (2016). Simultaneous Hand-Eye, Tool-Flange, and Robot-Robot Calibration for Comanipulation by Solving the AXB = YCZ Problem. IEEE Transactions on Robotics, 32(2), 413–428. https://doi.org/10.1109/TRO.2016.2530079

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  • Fast Depth Video Compression for Mobile RGB-D Sensors

    Wang, X., Şekercioǧlu, Y. A., Drummond, T., Natalizio, E., Fantoni, I., & Frémont, V. (2016). Fast Depth Video Compression for Mobile RGB-D Sensors. IEEE Transactions on Circuits and Systems for Video Technology, 26(4), 673–686. https://doi.org/10.1109/TCSVT.2015.2416571

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  • Correspondence Driven Saliency Transfer

    Wang, W., Shen, J., Shao, L., & Porikli, F. (2016). Correspondence Driven Saliency Transfer. IEEE Transactions on Image Processing, 25(11), 5025–5034. http://doi.org/10.1109/TIP.2016.2601784

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  • Robust Model Fitting Using Higher Than Minimal Subset Sampling

    Tennakoon, R. B., Bab-Hadiashar, A., Cao, Z., Hoseinnezhad, R., & Suter, D. (2016). Robust Model Fitting Using Higher Than Minimal Subset Sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 350–362. https://doi.org/10.1109/TPAMI.2015.2448103

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  • Teaching Robots Generalizable Hierarchical Tasks Through Natural Language Instruction

    Suddrey, G., Lehnert, C., Eich, M., Maire, F., & Roberts, J. (2016). Teaching Robots Generalisable Hierarchical Tasks Through Natural Language Instruction. IEEE Robotics and Automation Letters, 2(1), 201–208. http://doi.org/10.1109/LRA.2016.2588584

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  • Unlocking Neural Complexity with a Robotic Key

    Stratton, P., Hasselmo, M., & Milford, M. (2016). Unlocking neural complexity with a robotic key. The Journal of Physiology, 594(22), 6559–6567. http://doi.org/10.1113/JP271444

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  • A Passivity-Based Approach to Formation Control Using Partial Measurements of Relative Position

    Stacey, G., & Mahony, R. (2016). A Passivity-Based Approach to Formation Control Using Partial Measurements of Relative Position. IEEE Transactions on Automatic Control, 61(2), 538–543. https://doi.org/10.1109/TAC.2015.2446811

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  • Distributed Formation Control of Networked Mobile Robots in Environments with Obstacles

    Seng, W. L., Barca, J. C., Şekercioğlu, Y. A., & Ahmet Ekercio˘ Glu, Y. (2016). Distributed formation control of networked mobile robots in environments with obstacles. Robotica Robotica Robotica, 34(34), 1403–1415. http://doi.org/10.1017/S0263574714002380

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  • Strategies for Pre-Emptive Mid-Air Collision Avoidance in Budgerigars

    Schiffner, I., Perez, T., Srinivasan, M. V., Angelov, P., Padian, K., Chiappe, L., … Wyndham, E. (2016). Strategies for Pre-Emptive Mid-Air Collision Avoidance in Budgerigars. PLOS ONE, 11(9), e0162435. http://doi.org/10.1371/journal.pone.0162435

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  • Deep Learning for Automatic Detection and Classification of Microaneurysms, Hard and Soft Exudates, and Hemorrhages for Diabetic Retinopathy Diagnosis

    Saha, S. K., Fernando, B., Xiao, D., Tay-Kearney, M.-L., & Kanagasingam, Y. (2016). Deep Learning for Automatic Detection and Classification of Microaneurysms, Hard and Soft Exudates, and Hemorrhages for Diabetic Retinopathy Diagnosis. Investigative Ophthalmology & Visual Science, 57(12), pp.5962–5962.

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  • A Flexible Hierarchical Approach For Facial Age Estimation Based on Multiple Features

    Pontes, J. K., Britto, A. S., Fookes, C., & Koerich, A. L. (2016). A flexible hierarchical approach for facial age estimation based on multiple features. Pattern Recognition, 54, 34–51. http://doi.org/10.1016/j.patcog.2015.12.003

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  • Routed Roads: Probabilistic Vision-Based Place Recognition for Changing Conditions, Split Streets and Varied Viewpoints

    Pepperell, E., Corke, P., & Milford, M. (2016). Routed roads: Probabilistic vision-based place recognition for changing conditions, split streets and varied viewpoints. The International Journal of Robotics Research, 35(9), 1057–1079. http://doi.org/10.1177/0278364915618766

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  • Fast Rotation Search with Stereographic Projections for 3D Registration

    Parra Bustos, A., Chin, T.-J., Eriksson, A., Li, H., & Suter, D. (2016). Fast Rotation Search with Stereographic Projections for 3D Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2227–2240. http://doi.org/10.1109/TPAMI.2016.2517636

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  • Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning

    Paisitkriangkrai, S., Shen, C., & Hengel, A. van den. (2016). Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(6), 1243–1257. http://doi.org/10.1109/TPAMI.2015.2474388

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  • RatSLAM: Using Models of Rodent Hippocampus for Robot Navigation and Beyond

    Milford M., Jacobson A., Chen Z., Wyeth G. (2016) RatSLAM: Using Models of Rodent Hippocampus for Robot Navigation and Beyond. In: Inaba M., Corke P. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 114. Springer, Cham. https://doi.org/10.1007/978-3-319-28872-7_27

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  • Visual Tracking Under Motion Blur

    Ma, B., Huang, L., Shen, J., Shao, L., Yang, M.-H., & Porikli, F. (2016). Visual Tracking Under Motion Blur. IEEE Transactions on Image Processing, 25(12), 5867–5876. http://doi.org/10.1109/TIP.2016.2615812

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  • Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments

    Lowry, S., & Milford, M. J. (2016). Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments. IEEE Transactions on Robotics, 32(3), 600–613. http://doi.org/10.1109/TRO.2016.2545711

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  • A Generalized Probabilistic Framework for Compact Codebook Creation

    Liu, L., Wang, L., & Shen, C. (2016). A Generalized Probabilistic Framework for Compact Codebook Creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 224–237. https://doi.org/10.1109/TPAMI.2015.2441069

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  • Online Unsupervised Feature Learning for Visual Tracking

    Liu, F., Shen, C., Reid, I., & van den Hengel, A. (2016). Online unsupervised feature learning for visual tracking. Image and Vision Computing, 51(July), 84–94. http://doi.org/10.1016/j.imavis.2016.04.008

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  • Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

    Liu, F., Shen, C., Lin, G., & Reid, I. (2016). Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(10), 2024–2039. http://doi.org/10.1109/TPAMI.2015.2505283

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  • Online Metric-Weighted Linear Representations for Robust Visual Tracking

    Li, X., Shen, C., Dick, A., Zhang, Z. M., & Zhuang, Y. (2016). Online Metric-Weighted Linear Representations for Robust Visual Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(5), 931–950. http://doi.org/10.1109/TPAMI.2015.2469276

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  • Convolutional Neural Net Bagging for Online Visual Tracking

    Li, H., Li, Y., & Porikli, F. (2016). Convolutional neural net bagging for online visual tracking. Computer Vision and Image Understanding, 153(December 2016), 120–129. http://doi.org/10.1016/j.cviu.2016.07.002

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  • A Novel Performance Evaluation Methodology for Single-Target Trackers

    Kristan, M., Matas, J., Leonardis, A., Vojir, T., Pflugfelder, R., Fernandez, G., … Cehovin, L. (2016). A Novel Performance Evaluation Methodology for Single-Target Trackers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2137–2155. http://doi.org/10.1109/TPAMI.2016.2516982

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  • State Estimation for Invariant Systems on Lie Groups with Delayed Output Measurements

    Khosravian, A., Trumpf, J., Mahony, R., & Hamel, T. (2016). State estimation for invariant systems on Lie groups with delayed output measurements. Automatica, 68, 254–265. https://doi.org/10.1016/j.automatica.2016.01.024

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  • Fast Detection of Multiple Objects in Traffic Scenes with a Common Detection Framework

    Hu, Q., Paisitkriangkrai, S., Shen, C., van den Hengel, A., & Porikli, F. (2016). Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1002–1014. http://doi.org/10.1109/TITS.2015.2496795

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  • Dynamic Kinesthetic Boundary for Haptic Teleoperation of VTOL Aerial Robots in Complex Environments

    Hou, X., & Mahony, R. (2016). Dynamic kinesthetic boundary for haptic teleoperation of VTOL aerial robots in complex environments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(5), 694–705. https://doi.org/10.1109/TSMC.2015.2478756

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  • Automated Fourier Space Region-Recognition Filtering for Off-Axis Digital Holographic Microscopy

    He, X., Nguyen, C. V., Pratap, M., Zheng, Y., Wang, Y., Nisbet, D. R., Williams, R. J., Rug, M., Maier, A. G & Lee, W. M. (2016). Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy. Biomedical Optics Express, 7(8), 3111. http://doi.org/10.1364/BOE.7.003111

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  • Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences

    Harandi, M. T., Hartley, R., Lovell, B., & Sanderson, C. (2016). Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences. IEEE Transactions on Neural Networks and Learning Systems, 27(6), 1294–1306. https://doi.org/10.1109/TNNLS.2014.2387383

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  • Partitioning de Bruijn Graphs into Fixed-Length Cycles for Robot Identification and Tracking

    Grubman, T., Şekercioğlu, Y. A., & Wood, D. R. (2016). Partitioning de Bruijn graphs into fixed-length cycles for robot identification and tracking. Discrete Applied Mathematics, 213, 101–113. https://doi.org/10.1016/j.dam.2016.05.013

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  • Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

    Ferrari, R., McKinnon, D., He, H., Smith, R., Corke, P., González-Rivero, M., … Upcroft, B. (2016). Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling. Remote Sensing, 8(2), 113. http://doi.org/10.3390/rs8020113

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  • Simple Change Detection from Mobile Light Field Cameras

    Dansereau, D. G., Williams, S. B., & Corke, P. I. (2016). Simple change detection from mobile light field cameras. Computer Vision and Image Understanding, 145(April), 160–171. http://doi.org/10.1016/j.cviu.2015.12.008

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  • Bayesian Nonparametric Clustering for Positive Definite Matrices

    Cherian, A., Morellas, V., & Papanikolopoulos, N. (2016). Bayesian Nonparametric Clustering for Positive Definite Matrices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(5), 862–874. https://doi.org/10.1109/TPAMI.2015.2456903

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  • Measuring the Performance of Single Image Depth Estimation Methods

    Cadena, C., Latif, Y., & Reid, I. D. (2016). Measuring the performance of single image depth estimation methods. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4150–4157). Daejeon, Korea: IEEE. http://doi.org/10.1109/IROS.2016.7759611

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  • Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

    Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., … Leonard, J. J. (2016). Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age. IEEE Transactions on Robotics, 32(6), 1309–1332. http://doi.org/10.1109/TRO.2016.2624754

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  • Discovering Team Structures in Soccer from Spatiotemporal Data

    Bialkowski, A., Lucey, P., Carr, P., Matthews, I., Sridharan, S., & Fookes, C. (2016). Discovering Team Structures in Soccer from Spatiotemporal Data. IEEE Transactions on Knowledge and Data Engineering, 28(10), 2596–2605. http://doi.org/10.1109/TKDE.2016.2581158

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  • A Filter Formulation for Computing Real Time Optical Flow

    Adarve, J. D., & Mahony, R. (2016). A Filter Formulation for Computing Real Time Optical Flow. IEEE Robotics and Automation Letters, 1(2), 1192–1199. http://doi.org/10.1109/LRA.2016.2532928

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  • DeepFruits: A Fruit Detection System Using Deep Neural Networks

    Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors, 16(8), 1222. http://doi.org/10.3390/s16081222

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  • Contour Completion Without Region Segmentation

    Ming, Y., Li, H., & He, X. (2016). Contour Completion Without Region Segmentation. IEEE Transactions on Image Processing, 25(8), 3597–3611. http://doi.org/10.1109/TIP.2016.2564646

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  • A Modular Software Framework for Eye–Hand Coordination in Humanoid Robots

    Leitner, J., Harding, S., Förster, A., & Corke, P. (2016). A Modular Software Framework for Eye–Hand Coordination in Humanoid Robots. Frontiers in Robotics and AI, 3, 26. http://doi.org/10.3389/frobt.2016.00026

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  • From ImageNet to Mining: Adapting Visual Object Detection with Minimal Supervision

    Bewley A., Upcroft B. (2016) From ImageNet to Mining: Adapting Visual Object Detection with Minimal Supervision. In: Wettergreen D., Barfoot T. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-27702-8_33

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  • Visual Place Recognition: A Survey

    Lowry, S., Sunderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., & Milford, M. J. (2016). Visual Place Recognition: A Survey. IEEE Transactions on Robotics, 32(1), 1–19. http://doi.org/10.1109/TRO.2015.2496823

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  • Long-range stereo visual odometry for extended altitude flight of unmanned aerial vehicles

    Warren, M., Corke, P., & Upcroft, B. (2016). Long-range stereo visual odometry for extended altitude flight of unmanned aerial vehicles. The International Journal of Robotics Research, 35(4), 381–403. http://doi.org/10.1177/0278364915581194

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  • General, Nested, and Constrained Wiberg Minimization

    Strelow, D., Wang, Q., Si, L., & Eriksson, A. (2016). General, nested, and constrained Wiberg minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1803–1815. https://doi.org/10.1109/TPAMI.2015.2487987

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  • Vision-based Obstacle Detection and Navigation for an Agricultural Robot

    Ball, D., Upcroft, B., Wyeth, G., Corke, P., English, A., Ross, P., Petten, T., Fitch, R., Sukkarieh, S., & Bate, A. (2016). Vision-based Obstacle Detection and Navigation for an Agricultural Robot. Journal of Field Robotics, 33(8), 1107–1130. http://doi.org/10.1002/rob.21644

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  • Robotics Competitions and Challenges

    Nardi, D., Roberts, J., Veloso, M., & Fletcher, L. (2016). Robotics competitions and challenges. In Springer Handbook of Robotics (pp. 1759–1783). Springer International Publishing. https://doi.org/10.1007/978-3-319-32552-1_66

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  • Modeling and Control of Aerial Robots

    Mahony, R., Beard, R. W., & Kumar, V. (2016). Modeling and control of aerial robots. In Springer Handbook of Robotics (pp. 1307–1333). Springer International Publishing. https://doi.org/10.1007/978-3-319-32552-1_52

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  • Visual Servoing

    Chaumette, F., Hutchinson, S., & Corke, P. (2016). Visual Servoing. In Springer Handbook of Robotics (pp. 841–866). Cham: Springer International Publishing. http://doi.org/10.1007/978-3-319-32552-1_34

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Book Chapters

  • RatSLAM: Using Models of Rodent Hippocampus for Robot Navigation and Beyond

    Milford M., Jacobson A., Chen Z., Wyeth G. (2016) RatSLAM: Using Models of Rodent Hippocampus for Robot Navigation and Beyond. In: Inaba M., Corke P. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 114. Springer, Cham. https://doi.org/10.1007/978-3-319-28872-7_27

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  • From ImageNet to Mining: Adapting Visual Object Detection with Minimal Supervision

    Bewley A., Upcroft B. (2016) From ImageNet to Mining: Adapting Visual Object Detection with Minimal Supervision. In: Wettergreen D., Barfoot T. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-27702-8_33

    View More

  • Robotics Competitions and Challenges

    Nardi, D., Roberts, J., Veloso, M., & Fletcher, L. (2016). Robotics competitions and challenges. In Springer Handbook of Robotics (pp. 1759–1783). Springer International Publishing. https://doi.org/10.1007/978-3-319-32552-1_66

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  • Modeling and Control of Aerial Robots

    Mahony, R., Beard, R. W., & Kumar, V. (2016). Modeling and control of aerial robots. In Springer Handbook of Robotics (pp. 1307–1333). Springer International Publishing. https://doi.org/10.1007/978-3-319-32552-1_52

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  • Visual Servoing

    Chaumette, F., Hutchinson, S., & Corke, P. (2016). Visual Servoing. In Springer Handbook of Robotics (pp. 841–866). Cham: Springer International Publishing. http://doi.org/10.1007/978-3-319-32552-1_34

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Journal Articles

  • Quantifying Spatiotemporal Greenhouse Gas Emissions Using Autonomous Surface Vehicles

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

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

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

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  • Recent Advances in Camera Planning for Large Area Surveillance

    Liu, J., Sridharan, S., & Fookes, C. (2016). Recent Advances in Camera Planning for Large Area Surveillance. ACM Computing Surveys, 49(1), 1–37. http://doi.org/10.1145/2906148

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  • Dictionary Learning for Promoting Structured Sparsity in Hyprspectral Compressive Sensing

    Zhang, L., Wei, W., Zhang, Y., Shen, C., Van Den Hengel, A., & Shi, Q. (2016). Dictionary Learning for Promoting Structured Sparsity in Hyperspectral Compressive Sensing. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7223–7235. https://doi.org/10.1109/TGRS.2016.2598577

    View More

  • Unsupervised Feature Learning for Dense Correspondences Across Scenes

    Zhang, C., Shen, C., & Shen, T. (2016). Unsupervised Feature Learning for Dense Correspondences Across Scenes. International Journal of Computer Vision, 116(1), 90–107. https://doi.org/10.1007/s11263-015-0829-6

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  • Development of a Multi-Channel Concentric Tube Robotic System With Active Vision for Transnasal Nasopharyngeal Carcinoma Procedures

    Yu, H., Wu, L., Wu, K., & Ren, H. (2016). Development of a Multi-Channel Concentric Tube Robotic System with Active Vision for Transnasal Nasopharyngeal Carcinoma Procedures. IEEE Robotics and Automation Letters, 1(2), 1172–1178. https://doi.org/10.1109/LRA.2016.2530794

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  • Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration

    Yang, J., Li, H., Campbell, D., & Jia, Y. (2016). Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2241–2254. http://doi.org/10.1109/TPAMI.2015.2513405

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  • Detecting Rare Events Using Kullback–Leibler Divergence: A Weakly Supervised Approach

    Xu, J., Denman, S., Fookes, C., & Sridharan, S. (2016). Detecting rare events using Kullback–Leibler divergence: A weakly supervised approach. Expert Systems with Applications, 54, 13–28. http://doi.org/10.1016/j.eswa.2016.01.035

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  • Hypergraph Modelling for Geometric Model Fitting

    Xiao, G., Wang, H., Lai, T., & Suter, D. (2016). Hypergraph modelling for geometric model fitting. Pattern Recognition, 60, 748–760. https://doi.org/10.1016/j.patcog.2016.06.026

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  • Simultaneous Hand–Eye, Tool–Flange, and Robot–Robot Calibration for Comanipulation by Solving the AXB = YCZ Problem

    Wu, L., Wang, J., Qi, L., Wu, K., Ren, H., & Meng, M. Q. H. (2016). Simultaneous Hand-Eye, Tool-Flange, and Robot-Robot Calibration for Comanipulation by Solving the AXB = YCZ Problem. IEEE Transactions on Robotics, 32(2), 413–428. https://doi.org/10.1109/TRO.2016.2530079

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  • Fast Depth Video Compression for Mobile RGB-D Sensors

    Wang, X., Şekercioǧlu, Y. A., Drummond, T., Natalizio, E., Fantoni, I., & Frémont, V. (2016). Fast Depth Video Compression for Mobile RGB-D Sensors. IEEE Transactions on Circuits and Systems for Video Technology, 26(4), 673–686. https://doi.org/10.1109/TCSVT.2015.2416571

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  • Correspondence Driven Saliency Transfer

    Wang, W., Shen, J., Shao, L., & Porikli, F. (2016). Correspondence Driven Saliency Transfer. IEEE Transactions on Image Processing, 25(11), 5025–5034. http://doi.org/10.1109/TIP.2016.2601784

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  • Robust Model Fitting Using Higher Than Minimal Subset Sampling

    Tennakoon, R. B., Bab-Hadiashar, A., Cao, Z., Hoseinnezhad, R., & Suter, D. (2016). Robust Model Fitting Using Higher Than Minimal Subset Sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 350–362. https://doi.org/10.1109/TPAMI.2015.2448103

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  • Teaching Robots Generalizable Hierarchical Tasks Through Natural Language Instruction

    Suddrey, G., Lehnert, C., Eich, M., Maire, F., & Roberts, J. (2016). Teaching Robots Generalisable Hierarchical Tasks Through Natural Language Instruction. IEEE Robotics and Automation Letters, 2(1), 201–208. http://doi.org/10.1109/LRA.2016.2588584

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  • Unlocking Neural Complexity with a Robotic Key

    Stratton, P., Hasselmo, M., & Milford, M. (2016). Unlocking neural complexity with a robotic key. The Journal of Physiology, 594(22), 6559–6567. http://doi.org/10.1113/JP271444

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  • A Passivity-Based Approach to Formation Control Using Partial Measurements of Relative Position

    Stacey, G., & Mahony, R. (2016). A Passivity-Based Approach to Formation Control Using Partial Measurements of Relative Position. IEEE Transactions on Automatic Control, 61(2), 538–543. https://doi.org/10.1109/TAC.2015.2446811

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  • Distributed Formation Control of Networked Mobile Robots in Environments with Obstacles

    Seng, W. L., Barca, J. C., Şekercioğlu, Y. A., & Ahmet Ekercio˘ Glu, Y. (2016). Distributed formation control of networked mobile robots in environments with obstacles. Robotica Robotica Robotica, 34(34), 1403–1415. http://doi.org/10.1017/S0263574714002380

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  • Strategies for Pre-Emptive Mid-Air Collision Avoidance in Budgerigars

    Schiffner, I., Perez, T., Srinivasan, M. V., Angelov, P., Padian, K., Chiappe, L., … Wyndham, E. (2016). Strategies for Pre-Emptive Mid-Air Collision Avoidance in Budgerigars. PLOS ONE, 11(9), e0162435. http://doi.org/10.1371/journal.pone.0162435

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  • Deep Learning for Automatic Detection and Classification of Microaneurysms, Hard and Soft Exudates, and Hemorrhages for Diabetic Retinopathy Diagnosis

    Saha, S. K., Fernando, B., Xiao, D., Tay-Kearney, M.-L., & Kanagasingam, Y. (2016). Deep Learning for Automatic Detection and Classification of Microaneurysms, Hard and Soft Exudates, and Hemorrhages for Diabetic Retinopathy Diagnosis. Investigative Ophthalmology & Visual Science, 57(12), pp.5962–5962.

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  • A Flexible Hierarchical Approach For Facial Age Estimation Based on Multiple Features

    Pontes, J. K., Britto, A. S., Fookes, C., & Koerich, A. L. (2016). A flexible hierarchical approach for facial age estimation based on multiple features. Pattern Recognition, 54, 34–51. http://doi.org/10.1016/j.patcog.2015.12.003

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  • Routed Roads: Probabilistic Vision-Based Place Recognition for Changing Conditions, Split Streets and Varied Viewpoints

    Pepperell, E., Corke, P., & Milford, M. (2016). Routed roads: Probabilistic vision-based place recognition for changing conditions, split streets and varied viewpoints. The International Journal of Robotics Research, 35(9), 1057–1079. http://doi.org/10.1177/0278364915618766

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  • Fast Rotation Search with Stereographic Projections for 3D Registration

    Parra Bustos, A., Chin, T.-J., Eriksson, A., Li, H., & Suter, D. (2016). Fast Rotation Search with Stereographic Projections for 3D Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2227–2240. http://doi.org/10.1109/TPAMI.2016.2517636

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  • Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning

    Paisitkriangkrai, S., Shen, C., & Hengel, A. van den. (2016). Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(6), 1243–1257. http://doi.org/10.1109/TPAMI.2015.2474388

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  • Visual Tracking Under Motion Blur

    Ma, B., Huang, L., Shen, J., Shao, L., Yang, M.-H., & Porikli, F. (2016). Visual Tracking Under Motion Blur. IEEE Transactions on Image Processing, 25(12), 5867–5876. http://doi.org/10.1109/TIP.2016.2615812

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  • Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments

    Lowry, S., & Milford, M. J. (2016). Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments. IEEE Transactions on Robotics, 32(3), 600–613. http://doi.org/10.1109/TRO.2016.2545711

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  • A Generalized Probabilistic Framework for Compact Codebook Creation

    Liu, L., Wang, L., & Shen, C. (2016). A Generalized Probabilistic Framework for Compact Codebook Creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 224–237. https://doi.org/10.1109/TPAMI.2015.2441069

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  • Online Unsupervised Feature Learning for Visual Tracking

    Liu, F., Shen, C., Reid, I., & van den Hengel, A. (2016). Online unsupervised feature learning for visual tracking. Image and Vision Computing, 51(July), 84–94. http://doi.org/10.1016/j.imavis.2016.04.008

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  • Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

    Liu, F., Shen, C., Lin, G., & Reid, I. (2016). Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(10), 2024–2039. http://doi.org/10.1109/TPAMI.2015.2505283

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  • Online Metric-Weighted Linear Representations for Robust Visual Tracking

    Li, X., Shen, C., Dick, A., Zhang, Z. M., & Zhuang, Y. (2016). Online Metric-Weighted Linear Representations for Robust Visual Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(5), 931–950. http://doi.org/10.1109/TPAMI.2015.2469276

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  • Convolutional Neural Net Bagging for Online Visual Tracking

    Li, H., Li, Y., & Porikli, F. (2016). Convolutional neural net bagging for online visual tracking. Computer Vision and Image Understanding, 153(December 2016), 120–129. http://doi.org/10.1016/j.cviu.2016.07.002

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  • A Novel Performance Evaluation Methodology for Single-Target Trackers

    Kristan, M., Matas, J., Leonardis, A., Vojir, T., Pflugfelder, R., Fernandez, G., … Cehovin, L. (2016). A Novel Performance Evaluation Methodology for Single-Target Trackers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2137–2155. http://doi.org/10.1109/TPAMI.2016.2516982

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  • State Estimation for Invariant Systems on Lie Groups with Delayed Output Measurements

    Khosravian, A., Trumpf, J., Mahony, R., & Hamel, T. (2016). State estimation for invariant systems on Lie groups with delayed output measurements. Automatica, 68, 254–265. https://doi.org/10.1016/j.automatica.2016.01.024

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  • Fast Detection of Multiple Objects in Traffic Scenes with a Common Detection Framework

    Hu, Q., Paisitkriangkrai, S., Shen, C., van den Hengel, A., & Porikli, F. (2016). Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1002–1014. http://doi.org/10.1109/TITS.2015.2496795

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  • Dynamic Kinesthetic Boundary for Haptic Teleoperation of VTOL Aerial Robots in Complex Environments

    Hou, X., & Mahony, R. (2016). Dynamic kinesthetic boundary for haptic teleoperation of VTOL aerial robots in complex environments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(5), 694–705. https://doi.org/10.1109/TSMC.2015.2478756

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  • Automated Fourier Space Region-Recognition Filtering for Off-Axis Digital Holographic Microscopy

    He, X., Nguyen, C. V., Pratap, M., Zheng, Y., Wang, Y., Nisbet, D. R., Williams, R. J., Rug, M., Maier, A. G & Lee, W. M. (2016). Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy. Biomedical Optics Express, 7(8), 3111. http://doi.org/10.1364/BOE.7.003111

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  • Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences

    Harandi, M. T., Hartley, R., Lovell, B., & Sanderson, C. (2016). Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences. IEEE Transactions on Neural Networks and Learning Systems, 27(6), 1294–1306. https://doi.org/10.1109/TNNLS.2014.2387383

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  • Partitioning de Bruijn Graphs into Fixed-Length Cycles for Robot Identification and Tracking

    Grubman, T., Şekercioğlu, Y. A., & Wood, D. R. (2016). Partitioning de Bruijn graphs into fixed-length cycles for robot identification and tracking. Discrete Applied Mathematics, 213, 101–113. https://doi.org/10.1016/j.dam.2016.05.013

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  • Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

    Ferrari, R., McKinnon, D., He, H., Smith, R., Corke, P., González-Rivero, M., … Upcroft, B. (2016). Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling. Remote Sensing, 8(2), 113. http://doi.org/10.3390/rs8020113

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  • Simple Change Detection from Mobile Light Field Cameras

    Dansereau, D. G., Williams, S. B., & Corke, P. I. (2016). Simple change detection from mobile light field cameras. Computer Vision and Image Understanding, 145(April), 160–171. http://doi.org/10.1016/j.cviu.2015.12.008

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  • Bayesian Nonparametric Clustering for Positive Definite Matrices

    Cherian, A., Morellas, V., & Papanikolopoulos, N. (2016). Bayesian Nonparametric Clustering for Positive Definite Matrices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(5), 862–874. https://doi.org/10.1109/TPAMI.2015.2456903

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  • Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

    Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., … Leonard, J. J. (2016). Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age. IEEE Transactions on Robotics, 32(6), 1309–1332. http://doi.org/10.1109/TRO.2016.2624754

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  • Discovering Team Structures in Soccer from Spatiotemporal Data

    Bialkowski, A., Lucey, P., Carr, P., Matthews, I., Sridharan, S., & Fookes, C. (2016). Discovering Team Structures in Soccer from Spatiotemporal Data. IEEE Transactions on Knowledge and Data Engineering, 28(10), 2596–2605. http://doi.org/10.1109/TKDE.2016.2581158

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  • A Filter Formulation for Computing Real Time Optical Flow

    Adarve, J. D., & Mahony, R. (2016). A Filter Formulation for Computing Real Time Optical Flow. IEEE Robotics and Automation Letters, 1(2), 1192–1199. http://doi.org/10.1109/LRA.2016.2532928

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  • DeepFruits: A Fruit Detection System Using Deep Neural Networks

    Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors, 16(8), 1222. http://doi.org/10.3390/s16081222

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  • Contour Completion Without Region Segmentation

    Ming, Y., Li, H., & He, X. (2016). Contour Completion Without Region Segmentation. IEEE Transactions on Image Processing, 25(8), 3597–3611. http://doi.org/10.1109/TIP.2016.2564646

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  • A Modular Software Framework for Eye–Hand Coordination in Humanoid Robots

    Leitner, J., Harding, S., Förster, A., & Corke, P. (2016). A Modular Software Framework for Eye–Hand Coordination in Humanoid Robots. Frontiers in Robotics and AI, 3, 26. http://doi.org/10.3389/frobt.2016.00026

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  • Visual Place Recognition: A Survey

    Lowry, S., Sunderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., & Milford, M. J. (2016). Visual Place Recognition: A Survey. IEEE Transactions on Robotics, 32(1), 1–19. http://doi.org/10.1109/TRO.2015.2496823

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  • Long-range stereo visual odometry for extended altitude flight of unmanned aerial vehicles

    Warren, M., Corke, P., & Upcroft, B. (2016). Long-range stereo visual odometry for extended altitude flight of unmanned aerial vehicles. The International Journal of Robotics Research, 35(4), 381–403. http://doi.org/10.1177/0278364915581194

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  • General, Nested, and Constrained Wiberg Minimization

    Strelow, D., Wang, Q., Si, L., & Eriksson, A. (2016). General, nested, and constrained Wiberg minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1803–1815. https://doi.org/10.1109/TPAMI.2015.2487987

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  • Vision-based Obstacle Detection and Navigation for an Agricultural Robot

    Ball, D., Upcroft, B., Wyeth, G., Corke, P., English, A., Ross, P., Petten, T., Fitch, R., Sukkarieh, S., & Bate, A. (2016). Vision-based Obstacle Detection and Navigation for an Agricultural Robot. Journal of Field Robotics, 33(8), 1107–1130. http://doi.org/10.1002/rob.21644

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Conference Papers

  • Fast Training of Triplet-based Deep Binary Embedding Networks

    Zhuang, B., Lin, G., Shen, C., & Reid, I. (2016). Fast training of triplet-based deep binary embedding networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 5955–5964. https://doi.org/10.1109/CVPR.2016.641

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  • Robust Visual Tracking with Deep Convolutional Neural Network Based Object Proposals on PETS

    Zhu, G., Porikli, F., & Li, H. (2016). Robust Visual Tracking with Deep Convolutional Neural Network Based Object Proposals on PETS. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 (pp. 1265–1272). Las Vegas, Nevada: IEEE Computer Society. http://doi.org/10.1109/CVPRW.2016.160

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  • Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals

    Zhu, G., Porikli, F., & Li, H. (2016). Beyond local search: Tracking objects everywhere with instance-specific proposals. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 943–951. https://doi.org/10.1109/CVPR.2016.108

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  • Real-time Rotation Estimation for Dense Depth Sensors in Piece-wise Planar Environments

    Zhou, Y., Kneip, L., & Li, H. (2016). Real-time rotation estimation for dense depth sensors in piece-wise planar environments. IEEE International Conference on Intelligent Robots and Systems, 2016-November, 2271–2278. https://doi.org/10.1109/IROS.2016.7759355

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  • Less Is More: Towards Compact CNNs

    Zhou H., Alvarez J.M., Porikli F. (2016) Less Is More: Towards Compact CNNs. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9908. Springer, Cham. https://doi.org/10.1007/978-3-319-46493-0_40

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  • Cluster Sparsity Field for Hyperspectral Imagery Denoising

    Zhang L., Wei W., Zhang Y., Shen C., van den Hengel A., Shi Q. (2016) Cluster Sparsity Field for Hyperspectral Imagery Denoising. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9909. Springer, Cham. https://doi.org/10.1007/978-3-319-46454-1_38

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  • SLNSW-UTS: A Historical Image Dataset for Image Multi-Labeling and Retrieval

    Zhang, J., Zhang, J., Lu, J., Shen, C., Curr, K., Phua, R., Neville, R., & Edmonds, E. (2016). SLNSW-UTS: A Historical Image Dataset for Image Multi-Labeling and Retrieval. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–6). Gold Coast, Australia: IEEE. http://doi.org/10.1109/DICTA.2016.7797084

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  • Vertical Axis Detection for Sport Video Analytics

    Zeng, R., Lakemond, R., Denman, S., Sridharan, S., Fookes, C., & Morgan, S. (2016). Vertical Axis Detection for Sport Video Analytics. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE. http://doi.org/10.1109/DICTA.2016.7797093

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  • Ultra-Resolving Face Images by Discriminative Generative Networks

    Yu X., Porikli F. (2016) Ultra-Resolving Face Images by Discriminative Generative Networks. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9909. Springer, Cham. https://doi.org/10.1007/978-3-319-46454-1_20

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  • Riemannian Sparse Coding for Classification of PolSAR Images

    Yang, W., Zhong, N., Yang, X., & Cherian, A. (2016). Riemannian sparse coding for classification of PolSAR images. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5698–5701). Beijing, China: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/IGARSS.2016.7730488

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  • Robust Optical Flow Estimation of Double-Layer Images under Transparency or Reflection

    Yang, J., Li, H., Dai, Y., & Tan, R. T. (2016). Robust optical flow estimation of double-layer images under transparency or reflection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 1410–1419. https://doi.org/10.1109/CVPR.2016.157

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  • Superpixel-Based Two-View Deterministic Fitting for Multiple-Structure Data

    Xiao G., Wang H., Yan Y., Suter D. (2016) Superpixel-Based Two-View Deterministic Fitting for Multiple-Structure Data. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9910. Springer, Cham. https://doi.org/10.1007/978-3-319-46466-4_31

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  • Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources

    Wu, Q., Wang, P., Shen, C., Dick, A., & Van Den Hengel, A. (2016). Ask me anything: Free-form visual question answering based on knowledge from external sources. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 4622–4630. https://doi.org/10.1109/CVPR.2016.500

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  • What Value Do Explicit High Level Concepts Have in Vision to Language Problems?

    Wu, Q., Shen, C., Liu, L., Dick, A., & Van Den Hengel, A. (2016). What value do explicit high level concepts have in vision to language problems? Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 203–212. https://doi.org/10.1109/CVPR.2016.29

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  • Towards Hybrid Control of a Flexible Curvilinear Surgical Robot With Visual/Haptic Guidance

    Wu, L., Wu, K., & Ren, H. (2016). Towards hybrid control of a flexible curvilinear surgical robot with visual/haptic guidance. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 (pp. 501–507). Daejeon, Korea: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/IROS.2016.7759100

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  • Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering

    Wang, Y., Wenjie, Z., Wu, L., Lin, X., Fang, M., & Pan, S. (2016). Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering (pp. 2153–2159). Association for the Advancement of Artificial Intelligence (AAAI). https://research.monash.edu/en/publications/iterative-views-agreement-an-iterative-low-rank-based-structured-

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  • Collaborative Multi-Sensor Image Transmission and Data Fusion in Mobile Visual Sensor Networks Equipped with RGB-D Cameras

    Wang, X., Ahmet Sekercioglu, Y., Drummond, T., Natalizio, E., Fantoni, I., & Fremont, V. (2016). Collaborative multi-sensor image transmission and data fusion in mobile visual sensor networks equipped with RGB-D cameras. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 0, 1–8. https://doi.org/10.1109/MFI.2016.7849458

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  • UAV Based Target Finding and Tracking in GPS-Denied and Cluttered Environments

    Vanegas, F., Campbell, D., Eich, M., & Gonzalez, F. (2016). UAV based target finding and tracking in GPS-denied and cluttered environments. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 (pp. 2307–2313). Daejeon, South Korea: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/IROS.2016.7759360

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  • Find my office: Navigating real space from semantic descriptions

    Talbot, B., Lam, O., Schulz, R., Dayoub, F., Upcroft, B., & Wyeth, G. (2016). Find my office: Navigating real space from semantic descriptions. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 5782–5787. https://doi.org/10.1109/ICRA.2016.7487802

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  • Place Categorization and Semantic Mapping on a Mobile Robot

    Sunderhauf, N., Dayoub, F., McMahon, S., Talbot, B., Schulz, R., Corke, P., … Milford, M. (2016). Place categorization and semantic mapping on a mobile robot. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5729–5736). IEEE. http://doi.org/10.1109/ICRA.2016.7487796

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  • Learning Functional Argument Mappings for Hierarchical Tasks from Situation Specific Explanations

    Suddrey, G., Eich, M., Maire, F., & Roberts, J. (2016). Learning Functional Argument Mappings for Hierarchical Tasks from Situation Specific Explanations. In AI 2016: Advances in Artificial Intelligence (pp. 345–352). Springer, Cham. http://doi.org/10.1007/978-3-319-50127-7_30

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  • Towards Robotic Arthroscopy: “Instrument gap” Segmentation

    Strydom, M., Jaiprakash, A., Crawford, R., Peynot, T., & Roberts, J. (2016). Towards robotic arthroscopy: “Instrument gap” segmentation. Australasian Conference on Robotics and Automation, ACRA, 2016-December, 248–257.

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  • Skyline-based Localisation for Aggressively Manoeuvring Robots using UV sensors and Spherical Harmonics

    Stone, T., Differt, D., Milford, M., & Webb, B. (2016). Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5615–5622). Stockholm: IEEE. http://doi.org/10.1109/ICRA.2016.7487780

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  • High-Fidelity Simulation for Evaluating Robotic Vision Performance

    Skinner, J., Garg, S., Sunderhauf, N., Corke, P., Upcroft, B., & Milford, M. (2016). High-fidelity simulation for evaluating robotic vision performance. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Daejeon, Korea. http://doi.org/10.1109/IROS.2016.7759425

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  • Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

    Saleh F., Aliakbarian M.S., Salzmann M., Petersson L., Gould S., Alvarez J.M. (2016) Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9912. Springer, Cham. https://doi.org/10.1007/978-3-319-46484-8_25

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  • Joint Probabilistic Matching Using m-Best Solutions

    Rezatofighi, S. H., Milani, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2016). Joint probabilistic matching using m-best solutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 136–145. https://doi.org/10.1109/CVPR.2016.22

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  • Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression

    Qiao, R., Liu, L., Shen, C., & Hengel, A. Van Den. (2016). Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 2249–2257. https://doi.org/10.1109/CVPR.2016.247

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  • Design and fabrication of a disposable micro end effector for concentric tube robots

    Prasai, A. B., Jaiprakash, A., Pandey, A. K., Crawford, R., Roberts, J., & Wu, L. (2016). Design and fabrication of a disposable micro end effector for concentric tube robots. 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016. https://doi.org/10.1109/ICARCV.2016.7838560

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  • 3D Reconstruction Quality Analysis and Its Acceleration on GPU Clusters

    Polok, L., Ila, V., & Smrz, P. (2016). 3D reconstruction quality analysis and its acceleration on GPU clusters. In European Signal Processing Conference (EUSIPCO) (Vol. 2016–Novem, pp. 1108–1112). Budapest, Hungary. http://doi.org/10.1109/EUSIPCO.2016.7760420

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  • Efficient Point Process Inference for Large-scale Object Detection

    Pham, T. T., Rezatofighi, S. H., Reid, I., & Chin, T. J. (2016). Efficient Point Process Inference for Large-Scale Object Detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 2837–2845. https://doi.org/10.1109/CVPR.2016.310

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  • Geometrically Consistent Plane Extraction for Dense Indoor 3D Maps Segmentation

    Pham, T. T., Eich, M., Reid, I., & Wyeth, G. (2016). Geometrically consistent plane extraction for dense indoor 3D maps segmentation. IEEE International Conference on Intelligent Robots and Systems, 2016-November, 4199–4204. https://doi.org/10.1109/IROS.2016.7759618

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  • Deeper and Wider Fully Convolutional Network Coupled with Conditional Random Fields for Scene Labeling

    Nguyen, K., Fookes, C., & Sridharan, S. (2016). Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling. Proceedings - International Conference on Image Processing, ICIP, 2016-August, 1344–1348. https://doi.org/10.1109/ICIP.2016.7532577

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  • 3D Scanning System for Automatic High-Resolution Plant Phenotyping

    Nguyen, C. V., Fripp, J., Lovell, D. R., Furbank, R., Kuffner, P., Daily, H., & Sirault, X. (2016). 3D Scanning System for Automatic High-Resolution Plant Phenotyping. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–8). Gold Coast, Queensland: IEEE. http://doi.org/10.1109/DICTA.2016.7796984

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  • Non-Iterative, Fast SE(3) Path Smoothing

    Ng, Y., Jiang, B., Yu, C., & Li, H. (2016). Non-iterative, fast SE(3) path smoothing. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 (pp. 3172–3179). Daejeon, Korea: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/IROS.2016.7759490

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  • Latent Structural SVM with Marginal Probabilities for Weakly Labeled Structured Learning

    *Namin, S. R., Alvarez, J. M., Kneip, L., & Petersson, L. (2016). Latent structural SVM with marginal probabilities for weakly labeled structured learning. In 23rd IEEE International Conference on Image Processing, ICIP 2016 (pp. 3733–3737). Phoenix, United States: IEEE Computer Society.

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  • 2D Visual Place Recognition for Domestic Service Robots at Night

    Mount, J., & Milford, M. (2016). 2D visual place recognition for domestic service robots at night. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 4822–4829. https://doi.org/10.1109/ICRA.2016.7487686

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  • Visual Detection of Occluded Crop: for automated harvesting

    McCool, C., Sa, I., Dayoub, F., Lehnert, C., Perez, T., & Upcroft, B. (2016). Visual detection of occluded crop: For automated harvesting. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 2506–2512. https://doi.org/10.1109/ICRA.2016.7487405

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  • Underwater Image Descattering and Quality Assessment

    Lu, H., Li, Y., Xu, X., He, L., Li, Y., Dansereau, D., & Serikawa, S. (2016). Underwater image descattering and quality assessment. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 1998–2002). IEEE. http://doi.org/10.1109/ICIP.2016.7532708

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  • Learning Image Matching by Simply Watching Video

    Long G., Kneip L., Alvarez J.M., Li H., Zhang X., Yu Q. (2016) Learning Image Matching by Simply Watching Video. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9910. Springer, Cham. https://doi.org/10.1007/978-3-319-46466-4_26

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  • Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

    Lin, G., Shen, C., Hengel, A. Van Den, & Reid, I. (2016). Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 3194–3203. https://doi.org/10.1109/CVPR.2016.348

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  • On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units

    Liao, Z., & Carneiro, G. (2016). On the importance of normalisation layers in deep learning with piecewise linear activation units. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1–8). IEEE. http://doi.org/10.1109/WACV.2016.7477624

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  • Design and Flight Testing of a Bio-Inspired Plume Tracking Algorithm for Unmanned Aerial Vehicles

    Letheren, B., Montes, G., Villa, T., & Gonzalez, F. (2016). Design and flight testing of a bio-inspired plume tracking algorithm for unmanned aerial vehicles. IEEE Aerospace Conference Proceedings, 2016-June. https://doi.org/10.1109/AERO.2016.7500614

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  • LunaRoo: Designing a Hopping Lunar Science Payload

    Leitner, J., Chamberlain, W., Dansereau, D. G., Dunbabin, M., Eich, M., Peynot, T., … Sunderhauf, N. (2016). LunaRoo: Designing a hopping lunar science payload. In 2016 IEEE Aerospace Conference (pp. 1–12). IEEE. http://doi.org/10.1109/AERO.2016.7500760

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  • Sweet Pepper Pose Detection and Grasping for Automated Crop Harvesting

    Lehnert, C., Sa, I., McCool, C., Upcroft, B., & Perez, T. (2016). Sweet pepper pose detection and grasping for automated crop harvesting. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 2428–2434. https://doi.org/10.1109/ICRA.2016.7487394

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  • Conformal Surface Alignment With Optimal Mobius Search

    Le, H., Chin, T. J., & Suter, D. (2016). Conformal Surface Alignment with Optimal Möbius Search. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 2507–2516. https://doi.org/10.1109/CVPR.2016.275

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  • Multi-body non-rigid structure-from-motion

    Kumar, S., Dai, Y., & Li, H. (2016). Multi-body non-rigid structure-from-motion. In Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016 (pp. 148–156). Stanford, United States: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/3DV.2016.23

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  • Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions

    Kumar, B. G. V., Carneiro, G., & Reid, I. (2016). Learning local image descriptors with deep siamese and triplet convolutional networks by minimizing global loss functions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 5385–5394. https://doi.org/10.1109/CVPR.2016.581

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  • Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons

    Koniusz P., Cherian A., Porikli F. (2016) Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9908. Springer, Cham. https://doi.org/10.1007/978-3-319-46493-0_3

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  • Sparse Coding for Third-order Super-symmetric Tensor Descriptors with Application to Texture Recognition

    Koniusz, P., & Cherian, A. (2016). Sparse coding for third-order super-symmetric tensor descriptors with application to texture recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 5395–5403. https://doi.org/10.1109/CVPR.2016.582

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  • The Generalized Relative Pose and Scale Problem: View-Graph Fusion via 2D-2D Registration

    Kneip, L., Sweeney, C., & Hartley, R. (2016). The generalized relative pose and scale problem: View-graph fusion via 2D-2D registration. In IEEE Winter Conference on Applications of Computer Vision, WACV 2016. Lake Placid, United States: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/WACV.2016.7477656

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  • Direct Semi-dense SLAM for Rolling Shutter Cameras

    Kim, J. H., Cadena, C., & Reid, I. (2016). Direct semi-dense SLAM for rolling shutter cameras. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 1308–1315. https://doi.org/10.1109/ICRA.2016.7487263

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  • Deep Convolutional Neural Networks for Human Embryonic Cell Counting

    Khan A., Gould S., Salzmann M. (2016) Deep Convolutional Neural Networks for Human Embryonic Cell Counting. In: Hua G., Jégou H. (eds) Computer Vision – ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science, vol 9913. Springer, Cham. https://doi.org/10.1007/978-3-319-46604-0_25

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  • Unmanned Aerial Surveillance System for Hazard Collision Avoidance in Autonomous Shipping

    Johansen, T. A., & Perez, T. (2016). Unmanned aerial surveillance system for hazard collision avoidance in autonomous shipping. In 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1056–1065). IEEE. http://doi.org/10.1109/ICUAS.2016.7502542

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  • Robust Multi-body Feature Tracker: A Segmentation-free Approach

    Ji, P., Li, H., Salzmann, M., & Zhong, Y. (2016). Robust Multi-Body Feature Tracker: A Segmentation-Free Approach. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 3843–3851. https://doi.org/10.1109/CVPR.2016.417

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  • Haptics-Aided Path Planning and Virtual Fixture Based Dynamic Kinesthetic Boundary for Bilateral Teleoperation of VTOL Aerial Robots

    Hou, X., Wang, X., & Mahony, R. (2016). Haptics-aided path planning and virtual fixture based dynamic kinesthetic boundary for bilateral teleoperation of VTOL aerial robots. Chinese Control Conference, CCC, 2016-August, 4705–4710. https://doi.org/10.1109/ChiCC.2016.7554082

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  • Adaptive spatial filtering for off-axis digital holographic microscopy based on region recognition approach with iterative thresholding

    He, X., Nguyen, C. V., Pratap, M., Zheng, Y., Wang, Y., Nisbet, D. R., Rug, M., Maier, A. G., & Lee, W. M. (2016). Adaptive spatial filtering for off-axis digital holographic microscopy based on region recognition approach with iterative thresholding. In M. R. Hutchinson & E. M. Goldys (Eds.), SPIE BioPhotonics Australasia (Vol. 10013, p. 1001329). SPIE. https://doi.org/10.1117/12.2242876

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  • FANNG: Fast Approximate Nearest Neighbour Graphs

    Harwood, B., & Drummond, T. (2016). FANNG: Fast approximate nearest neighbour graphs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 5713–5722. https://doi.org/10.1109/CVPR.2016.616

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  • Discovery of Facial Motions using Deep Machine Perception

    Ghasemi, A., Denman, S., Sridharan, S., & Fookes, C. (2016, May 23). Discovery of facial motions using deep machine perception. 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. https://doi.org/10.1109/WACV.2016.7477448

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  • Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification

    Ge, Z., McCool, C., Sanderson, C., Wang, P., Liu, L., Reid, I., & Corke, P. (2016). Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification. In Digital Image Computing: Techniques and Applications (DICTA). Gold Coast, Queensland. http://doi.org/10.1109/DICTA.2016.7797039

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  • Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks

    Ge, Z., Bewley, A., McCool, C., Corke, P., Upcroft, B., & Sanderson, C. (2016). Fine-grained classification via mixture of deep convolutional neural networks. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1–6). IEEE. http://doi.org/10.1109/WACV.2016.7477700

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  • Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

    Garg R., B.G. V.K., Carneiro G., Reid I. (2016) Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9912. Springer, Cham. https://doi.org/10.1007/978-3-319-46484-8_45

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  • Automated Plant and Leaf Separation: Application in 3D Meshes of Wheat Plants

    Frolov, K., Fripp, J., Nguyen, C. V., Furbank, R., Bull, G., Kuffner, P., … Sirault, X. (2016). Automated Plant and Leaf Separation: Application in 3D Meshes of Wheat Plants. In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). Gold Coast, Queensland: IEEE. http://doi.org/10.1109/DICTA.2016.7797011

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  • Discriminative Hierarchical Rank Pooling for Activity Recognition

    Fernando, B., Anderson, P., Hutter, M., & Gould, S. (2016). Discriminative Hierarchical Rank Pooling for Activity Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 1924–1932. https://doi.org/10.1109/CVPR.2016.212

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  • A Consensus-Based Framework for Distributed Bundle Adjustment

    Eriksson, A., Bastian, J., Chin, T. J., & Isaksson, M. (2016). A Consensus-Based Framework for Distributed Bundle Adjustment. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 1754–1762. https://doi.org/10.1109/CVPR.2016.194

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  • Autonomous Greenhouse Gas Sampling Using Multiple Robotic Boats

    Dunbabin, M. (2016). Autonomous greenhouse gas sampling using multiple robotic boats. In 10th International Conference on Field and Service Robotics, FSR 2015 (Vol. 113, pp. 17–30). Toronto, Canada: Springer Verlag. http://doi.org/10.1007/978-3-319-27702-8_2

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  • Reliable Scale Estimation and Correction for Monocular Visual Odometry

    Dingfu Zhou, Dai, Y., & Hongdong Li. (2016). Reliable scale estimation and correction for monocular Visual Odometry. In 2016 IEEE Intelligent Vehicles Symposium (IV) (pp. 490–495). Gothenburg, Sweden: IEEE. http://doi.org/10.1109/IVS.2016.7535431

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  • MO-SLAM: Multi Object SLAM with Run-Time Object Discovery through Duplicates

    Dharmasiri, T., Lui, V., & Drummond, T. (2016). MO-SLAM: Multi object SLAM with run-time object discovery through duplicates - IEEE Xplore Document. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Daejeon, Korea. http://doi.org/10.1109/IROS.2016.7759203

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  • Output Regulation on the Special Euclidean Group SE(3)

    De Marco, S., Marconi, L., Hamel, T., & Mahony, R. (2016). Output regulation on the Special Euclidean Group SE(3). 2016 IEEE 55th Conference on Decision and Control, CDC 2016, 4734–4739. https://doi.org/10.1109/CDC.2016.7798991

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  • Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry

    Dai, Y., Li, H., & Kneip, L. (2016). Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 4132–4140. https://doi.org/10.1109/CVPR.2016.448

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  • Simultaneous Correspondences Estimation and Non-Rigid Structure Reconstruction

    Dai, Y., & Li, H. (2016). Simultaneous Correspondences Estimation and Non-Rigid Structure Reconstruction. In 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016. Gold Coast, Queensland: Institute of Electrical and Electronics Engineers Inc. http://doi.org/10.1109/DICTA.2016.7797083

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  • Guaranteed Outlier Removal With Mixed Integer Linear Programs

    Chin, T. J., Kee, Y. H., Eriksson, A., & Neumann, F. (2016). Guaranteed outlier removal with mixed integer linear programs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 5858–5866. https://doi.org/10.1109/CVPR.2016.631

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  • A Distributed Robotic Vision Service

    Chamberlain, W., Leitner, J., Drummond, T., & Corke, P. (2016). A distributed robotic vision service. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 2494–2499. https://doi.org/10.1109/ICRA.2016.7487403

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  • Dynamic Image Networks for Action Recognition

    Bilen, H., Fernando, B., Gavves, E., Vedaldi, A., & Gould, S. (2016). Dynamic Image Networks for Action Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 3034–3042. https://doi.org/10.1109/CVPR.2016.331

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  • ALExTRAC: Affinity Learning by Exploring Temporal Reinforcement within Association Chains

    Bewley, A., Ott, L., Ramos, F., & Upcroft, B. (2016). Alextrac: Affinity learning by exploring temporal reinforcement within association chains. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2212–2218). Stockholm, Sweden: IEEE. http://doi.org/10.1109/ICRA.2016.7487371

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  • Simple Online and Realtime Tracking

    Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016). Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 3464–3468). IEEE. http://doi.org/10.1109/ICIP.2016.7533003

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  • SPICE: Semantic Propositional Image Caption Evaluation

    Anderson P., Fernando B., Johnson M., Gould S. (2016) SPICE: Semantic Propositional Image Caption Evaluation. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9909. Springer, Cham. https://doi.org/10.1007/978-3-319-46454-1_24

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  • Velocity Aided Attitude Estimation for Aerial Robotic Vehicles Using Latent Rotation Scaling

    Allibert, G., Mahony, R., & Bangura, M. (2016). Velocity aided attitude estimation for aerial robotic vehicles using latent rotation scaling. Proceedings - IEEE International Conference on Robotics and Automation, 2016-June, 1538–1543. https://doi.org/10.1109/ICRA.2016.7487291

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  • Complex Event Detection using Joint Max Margin and Semantic Features

    Abbasnejad, I., Sridharan, S., Denman, S., Fookes, C., & Lucey, S. (2016, December 22). Complex Event Detection Using Joint Max Margin and Semantic Features. 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016. https://doi.org/10.1109/DICTA.2016.7797023

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  • Measuring the Performance of Single Image Depth Estimation Methods

    Cadena, C., Latif, Y., & Reid, I. D. (2016). Measuring the performance of single image depth estimation methods. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4150–4157). Daejeon, Korea: IEEE. http://doi.org/10.1109/IROS.2016.7759611

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  • From ImageNet to Mining: Adapting Visual Object Detection with Minimal Supervision

    Bewley A., Upcroft B. (2016) From ImageNet to Mining: Adapting Visual Object Detection with Minimal Supervision. In: Wettergreen D., Barfoot T. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-27702-8_33

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Edited Collection

  • Advances in Visual Computing

    Bebis, G., Boyle, R., Parvin, et al. (2016). Advances in Visual Computing. (Vol. 10072). Cham: Springer International Publishing. http://doi.org/10.1007/978-3-319-50835-1

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  • Computer Vision and Image Understanding (Vol. 146)

    Reid, I. (2016). 12th Asian conference on computer vision. Computer Vision and Image Understanding (Vol. 146).

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  • Robotics Research: The 16th International Symposium ISRR

    Inaba, M., & Corke, P. (2016). Robotics research: The 16th international symposium ISRR. In 16th International Symposium of Robotics Research, ISRR 2013 (Vol. 114). Singapore: Springer Verlag. http://doi.org/10.1007/978-3-319-28872-7

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