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2016 Conference Papers [88]

Image Captioning and Visual Question Answering Based on Attributes and External Knowledge

*Wu, Q., Shen, C., Hengel, A. van den, Wang, P., & Dick, A. (2016). Image Captioning and Visual Question Answering Based on Attributes and External Knowledge. Retrieved from http://arxiv.org/abs/1603.02814

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Self-Supervised Video Representation Learning With Odd-One-Out Networks

*Fernando, B., Bilen, H., Gavves, E., & Gould, S. (2016). Self-Supervised Video Representation Learning With Odd-One-Out Networks. Retrieved from http://arxiv.org/abs/1611.06646

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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. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016. Las Vegas, Nevada.

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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.

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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. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 943–951). Las Vegas, Nevada.

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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. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Daejeon, Korea.

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A Revisit of Methods for Determining the Fundamental Matrix with Planes

*Zhou, Y., Kneip, L., & Li, H. (2015). A Revisit of Methods for Determining the Fundamental Matrix with Planes. In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE

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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 Computer Vision – ECCV 2016 (pp. 662–677). Springer, Cham.

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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 Computer Vision - ECCV 2016 (pp. 631–647). Springer International Publishing.

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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., et al. (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.

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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.

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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 Computer Vision – ECCV 2016 (pp. 318–333). Springer.

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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.

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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. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 1410–1419). Las Vegas, Nevada: IEEE Computer Society.

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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 Computer Vision – ECCV 2016 (pp. 517–533). Springer, Cham.

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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. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 4622–4630). Las Vegas, Nevada: IEEE Computer Society.

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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? In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 203–212). Las Vegas, Nevada: IEEE Computer Society.

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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.

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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. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2016–January, pp. 2153–2159). New York, United States.

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

*Wang, X., Sekercioglu, A., 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. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016). Baden-Baden, Germany.

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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.

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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. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5782–5787). IEEE.

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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., et.al. (2016). Place categorization and semantic mapping on a mobile robot. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5729–5736). IEEE.

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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.

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Towards Robotic Arthroscopy: ‘Instrument gap’ Segmentation

*Strydom, M., Jaiprakash, A., Crawford, R., Peynot, T., & Roberts, J. M. (2016). Towards robotic arthroscopy: “Instrument gap” segmentation. In Australasian Conference on Robotics and Automation (ACRA) 2016. Brisbane, Queensland: Australian Robotics & Automation Association.

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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.

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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

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Bags of Affine Subspaces for Robust Object Tracking

*Shirazi, S., Sanderson, C., McCool, C., & Harandi, M. T. (2015). Bags of Affine Subspaces for Robust Object Tracking. In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). IEEE.

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

*Saleh, F., Ali Akbarian, M. S., Salzmann, M., Petersson, L., Gould, S., & Alvarez, J. M. (2016). Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation. In Computer Vision - ECCV 2016 (pp. 413–432). Springer International Publishing.

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Joint Probabilistic Matching Using m-Best Solutions

*Rezatofighi, S. H., Milan, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2016). Joint probabilistic matching using m-best solutions. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 136–145). Las Vegas, Nevada: IEEE Computer Society.

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

*Qiao, R., Liu, L., Shen, C., & Van Den Hengel, A. (2016). Less is more: Zero-shot learning from online textual documents with noise suppression. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 2249–2257). Las Vegas, Nevada: IEEE Computer Society.

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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 Design and Fabrication of a Disposable Micro End Effector for Concentric Tube Robots. In 14th International Conference on Control, Automation, Robotics and Vision (pp. 13–15). Press.

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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–November, pp. 1108–1112). Budapest, Hungary.

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Efficient Point Process Inference for Large-scale Object Detection

*Pham, T. T., Hamid Rezatofighi, S., Reid, I., & Chin, T.-J. (2016). Efficient Point Process Inference for Large-Scale Object Detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 2837–2845). Las Vegas, Nevada.

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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. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Daejeon, Korea: IEEE.

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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. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 1344–1348). IEEE.

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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.

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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.

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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. In IEEE International Conference on Robotics and Automation (ICRA 2016) (pp. 4822–4829). Stockholm, Sweden.

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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. In IEEE International Conference on Robotics and Automation (ICRA 2016). Stockholm, Sweden.

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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.

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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 Computer Vision - ECCV 2016 (pp. 434–450). Springer International Publishing.

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Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

*Lin, G., Shen, C., Hengel, A. van dan, & Reid, I. (2015). Efficient piecewise training of deep structured models for semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 3194–3203). Las Vegas, Nevada.

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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.

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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.

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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. In 2016 IEEE Aerospace Conference (pp. 1–9). IEEE.

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LunaRoo: Designing a Hopping Lunar Science Payload

*Leitner, J., Chamberlain, W., Dansereau, D. G., Dunbabin, M., Eich, M., Peynot, T., et.al. (2016). LunaRoo: Designing a hopping lunar science payload. In 2016 IEEE Aerospace Conference (pp. 1–12). IEEE.

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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. In IEEE International Conference on Robotics and Automation (ICRA 2016). Stockholm, Sweden.

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Conformal Surface Alignment With Optimal Mobius Search

*Le, H., Chin, T.-J., & Suter, D. (2016). Conformal Surface Alignment With Optimal Mobius Search. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (pp. 2507–2516). Las Vegas, Nevada.

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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.

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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. (2015). Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 5385–5394). Las Vegas, Nevada.

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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 Computer Vision – ECCV 2016 (pp. 37–53). Springer, Cham.

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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. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 5395–5403). Las Vegas, Nevada.

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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.

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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. In 2016 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1308–1315). Stockholm, Sweden: IEEE.

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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 Computer Vision - ECCV 2016 Workshops (pp. 339–348). Springer International Publishing.

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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.

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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. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 3843–3851). Las Vegas, Nevada: IEEE Computer Society.

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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. In 2016 35th Chinese Control Conference (CCC) (pp. 4705–4710). IEEE.

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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., et.al. (2016). Adaptive spatial filtering for off-axis digital holographic microscopy based on region recognition approach with iterative thresholding. In SPIE BioPhotonics Australasia (Vol. 10013). Adelaide, Australia: SPIE.

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Feature-based Recursive Observer Design for Homography Estimation

*Hua, M.-D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2016). Feature-based Recursive Observer Design for Homography Estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016.

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FANNG: Fast Approximate Nearest Neighbour Graphs

*Harwood, B., & Drummond, T. (2016). FANNG: Fast Approximate Nearest Neighbour Graphs. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 5713–5722). Las Vegas, Nevada.

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Discovery of Facial Motions using Deep Machine Perception

Ghasemi, A., Denman, S., Sridharan, S., & Fookes, C. (2016). Discovery of facial motions using deep machine perception. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1–7). IEEE.

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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.

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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.

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

*Garg, R., Vijay Kumar, B. G., Carneiro, G., & Reid, I. (2016). Unsupervised CNN for single view depth estimation: Geometry to the rescue. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9912 LNCS, pp. 740–756). Springer Verlag.

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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., et.al. (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.

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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. Proc. CVPR.

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The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results

*Felsberg, M., Berg, A., Häger, G., Ahlberg, J., Kristan, M., Matas, J., et.al. (2016). The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results. In 15th IEEE International Conference on Computer Vision Workshops (pp. 639–651). Santiago, Chile: Institute of Electrical and Electronics Engineers Inc.

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A Consensus-Based Framework for Distributed Bundle Adjustment

Eriksson, A., Bastian, J., Chin, T., & Isaksson, M. (2016). A Consensus-Based Framework for Distributed Bundle Adjustment. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016. Las Vegas, Nevada.

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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.

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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.

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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.

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Output Regulation on the Special Euclidean Group

de Marco, S., Marconi, L., Hamel, T., & Mahony, R. (2016). Output regulation on the Special Euclidean Group SE(3). In 2016 IEEE 55th Conference on Decision and Control (CDC) (pp. 4734–4739). IEEE.

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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. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 4132–4140). Las Vegas, United States: IEEE Computer Society.

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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.

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Guaranteed Outlier Removal With Mixed Integer Linear Programs

*Chin, T.-J., Heng Kee, Y., Eriksson, A., & Neumann, F. (2016). Guaranteed Outlier Removal With Mixed Integer Linear Programs. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (pp. 5858–5866). Las Vegas, Nevada.

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*Chamberlain, W., Leitner, J., Drummond, T., & Corke, P. (2016). A Distributed Robotic Vision Service. In IEEE International Conference on Robotics and Automation (ICRA 2016). Stockholm, Sweden.

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Dynamic Image Networks for Action Recognition

*Bilen, H., Fernando, B., Gavves, E., & Vedaldi, A. (2016). Dynamic image networks for action recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016.

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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.

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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.

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Non-Invasive Performance Measurement in Combat Sports

Behendi, S. K., Morgan, S., & Fookes, C. B. (2016). Non-Invasive Performance Measurement in Combat Sports. In Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS) (pp. 3–10). Springer, Cham.

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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 Computer Vision - ECCV 2016 (pp. 382–398). Springer International Publishing.

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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. In IEEE International Conference on Robotics and Automation (ICRA 2016).

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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). Complex event detection using joint max margin and semantic features. In Digital Image Computing: Techniques and Applications (DICTA). Gold Coast, Queensland.

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Memory Efficient Max Flow for Multi-label Submodular MRFs

*Ajanthan, T., Hartley, R., & Salzmann, M. (2016). Memory efficient max flow for multi-label submodular MRFs. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 5867–5876). Las Vegas, Nevada: IEEE Computer Society.

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Memory efficient max flow for multi-label submodular MRFs.

*Ajanthan, T., Hartley, R., & Salzmann, M. (2016). Memory efficient max flow for multi-label submodular MRFs. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 5867–5876). Las Vegas, Nevada: IEEE Computer Society.

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