2014 Publications

Scientific Publications

  • Expanding the Family of Grassmannian Kernels: An Embedding Perspective

    Harandi, M. T., Salzmann, M., Jayasumana, S., Hartley, R., & Li, H. 2014. Expanding the Family of Grassmannian Kernels: An Embedding Perspective. Computer Vision-ECCV 2014. Springer International Publishing. pp408-423

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  • Fast Approximate L ∞ Minimization: Speeding Up Robust Regression

    Shen, F., Shen, C., Hill, R., Van Den Hengel, A., & Tang, Z. (2014). Fast approximate L∞ minimization: Speeding up robust regression. Computational Statistics and Data Analysis, 77, 25–37. https://doi.org/10.1016/j.csda.2014.02.018

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  • An Efficient Dual Approach to Distance Metric Learning

    Shen, C., Kim, J., Liu, F., Wang, L., & Van Den Hengel, A. (2014). Efficient dual approach to distance metric learning. IEEE Transactions on Neural Networks and Learning Systems, 25(2), 394–406. https://doi.org/10.1109/TNNLS.2013.2275170

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  • DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking

    Li, H., Li, Y., & Porikli, F. (2014). DeepTrack: Learning discriminative feature representations by Convolutional Neural Networks for visual tracking. BMVC 2014 - Proceedings of the British Machine Vision Conference 2014. https://doi.org/10.5244/c.28.56

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  • Enhancing Moving Objects in Light Field Videos Using 5-D IIR Adaptive Depth-Velocity Filters

    Edussooriya, C., Dansereau, D., Bruton, L., & Agathoklis, P. 2015. "Five-Dimensional (5-D) Depth-Velocity Filtering for Enhancing Moving Objects in Light Field Videos." IEEE Transactions on Signal Processing (TSP).

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  • Optimal Essential Matrix Estimation via Inlier-Set Maximization

    Yang, J., Li, H., & Jia, Y. Optimal Essential Matrix Estimation via Inlier-Set Maximization. ECCV (1) 2014: 111-126

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  • CDnet 2014: An Expanded Change Detection Benchmark Dataset

    Wang, Y., Jodoin, P. M., Porikli, F., Konrad, J., Benezeth, Y., & Ishwar, P. (2014). CDnet 2014: An expanded change detection benchmark dataset. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 393–400. https://doi.org/10.1109/CVPRW.2014.126

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  • Counting People by Clustering Person Detector Outputs

    Topkaya, I. S., Erdogan, H., & Porikli, F. (2014). Counting people by clustering person detector outputs. 11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014, 313–318. https://doi.org/10.1109/AVSS.2014.6918687

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  • Sparse Dictionaries for Semantic Segmentation

    Tao L., Porikli F., Vidal R. (2014) Sparse Dictionaries for Semantic Segmentation. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693. Springer, Cham. https://doi.org/10.1007/978-3-319-10602-1_36

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  • Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction

    Sünderhauf, N., McCool, C., Upcroft, B., Perez., T. (2014). Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction. Proc. of ImageCLEF Workshop, International Conference of the CLEF Initiative, Sheffield, UK.

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  • Recycled Linear Classifiers for Multiclass Classification

    Soni, A., Haupt, J., & Porikli, F. (2014). Recycled linear classifiers for multiclass classification. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2957–2961. https://doi.org/10.1109/ICASSP.2014.6854142

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  • Determining Interacting Objects in Human-Centric Activities via Qualitative Spatio-Temporal Reasoning

    Sokeh H.S., Gould S., Renz J. (2015) Determining Interacting Objects in Human-Centric Activities via Qualitative Spatio-Temporal Reasoning. In: Cremers D., Reid I., Saito H., Yang MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science, vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_36

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  • Super-Resolving Noisy Images

    Singh, A., Porikli, F., & Ahuja, N. (2014). Super-resolving noisy images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2846–2853. https://doi.org/10.1109/CVPR.2014.364

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  • Robust Orthonormal Subspace Learning: Efficient Recovery of Corrupted Low-rank Matrices

    Shu, X., Porikli, F., & Ahuja, N. (2014). Robust orthonormal subspace learning: Efficient recovery of corrupted low-rank matrices. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3874–3881. https://doi.org/10.1109/CVPR.2014.495

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  • Object Tracking via Non-Euclidean Geometry: A Grassmann Approach

    Shirazi, S., Harandi, M. T., Lovell, B. C., & Sanderson, C. (2014). Object tracking via non-Euclidean geometry: A Grassmann approach. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 901–908. https://doi.org/10.1109/WACV.2014.6836008

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  • Hybrid Inference Optimization for Robust Pose Graph Estimation

    Segal, A. & Reid, I. Hybrid Inference Optimization for Robust Pose Graph Estimation, International Conference on Intelligent Robots and Systems 2014

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  • Inspection of Pole-Like Structures Using a Vision-Controlled VTOL UAV and Shared Autonomy

    Sa, I., Hrabar, S., & Corke, P. (2014). Inspection of pole-like structures using a vision-controlled VTOL UAV and shared autonomy. IEEE International Conference on Intelligent Robots and Systems, 4819–4826. https://doi.org/10.1109/IROS.2014.6943247

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  • Novelty-Based Visual Obstacle Detection in Agriculture

    Ross, P., English, A. R., Ball, D., Upcroft, B, Wyeth, G., & Corke, P., “Novelty-based visual obstacle dete in agriculture,” in Proc. IEEE Int. Conf. Robotics and Automation, pp. 1699–1705, 2014.

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  • A Method to Quantify a Descriptor’s Illumination Variance

    Ross, P., English, A, Ball, D., & Corke, P., “A method to quantify a descriptor’s illumination variance Australian Conference on Robotics and Automation, 2014.

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  • 3D Tracking of Multiple Objects with Identical Appearance using RGB-D Input

    Ren, C., Prisacariu, V., Murray, D., & Reid, I. 3D Tracking of Multiple Objects with Identical Appearance using RGB-D Input, 3DV, Dec 2014

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  • All-Environment Visual Place Recognition with SMART

    Pepperell, E., Corke, P., & Milford, M. “All-environment visual place recognition with smart,” in Proc. I Int. Conf. Robotics and Automation, pp. 1612–1618, 2014.

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  • Towards Vision-Based Pose- and Condition-Invariant Place Recognition along Routes

    Pepperell, E., Corke, P., & Milford, M. "Towards Vision-Based Pose- and Condition-Invariant Place Recognition along Routes", 2014 Australasian Conference on Robotics and Automation

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  • Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features

    Paisitkriangkrai S., Shen C., van den Hengel A. (2014) Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8692. Springer, Cham. https://doi.org/10.1007/978-3-319-10593-2_36

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  • Local Refinement for Stereo Regularization

    Olsson, C., Ulen, J., & Eriksson, A. (2014). Local refinement for stereo regularization. Proceedings - International Conference on Pattern Recognition, 4056–4061. https://doi.org/10.1109/ICPR.2014.695

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  • Empirical Modelling of Rolling Shutter Effect

    O’Sullivan, L., & Corke, P. (2014). Empirical modelling of rolling shutter effect. Proceedings - IEEE International Conference on Robotics and Automation, 2132–2137. https://doi.org/10.1109/ICRA.2014.6907152

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  • Detecting 3D Geometric Boundaries of Indoor Scenes Under Varying Lighting

    Ni, J., Marks, T. K., Tuzel, O., & Porikli, F. (2014). Detecting 3D geometric boundaries of indoor scenes under varying lighting. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 1–8. https://doi.org/10.1109/WACV.2014.6836125

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  • On Projective Reconstruction In Arbitrary Dimensions

    Nasihatkon, B., Hartley, R., & Trumpf, J. (2014). On projective reconstruction in arbitrary dimensions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 477–484. https://doi.org/10.1109/CVPR.2014.68

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  • Non-rigid Segmentation using Sparse Low Dimensional Manifolds and Deep Belief Networks

    Nascimento, J. C., & Carneiro, G. (2014). Non-rigid segmentation using sparse low dimensional manifolds and deep belief networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 288–295. https://doi.org/10.1109/CVPR.2014.44

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  • A Unified Graphical Models Framework for Automated Human Embryo Tracking in Time Lapse Microscopy

    Moussavi, F., Yu, W., Lorenzen, P., Oakley, J., Russakoff, D., & Gould, S., “A Unified Graphical Models Framework for Automated Human Embryo Tracking in Time Lapse Microscopy”. In Proceedings of the International Symposium on Biomedical Imaging (ISBI), 2014.

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  • Multiple Map Hypotheses for Planning and Navigating in Non-Stationary Environments

    Morris, T, Dayoub, F., Corke, P., Wyeth, G., & Upcroft, B, “Multiple map hypotheses for planning navigating in non-stationary environments,” in Proc. IEEE Int. Conf. Robotics and Automation, pp. 2765–2 2014.

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  • Simultaneous Localization and Planning on Multiple Map Hypotheses

    Morris, T, Dayoub, F., Corke, P., & Upcroft, B, “Simultaneous localization and planning on multiple hypotheses,” in Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference pp. 4531–4536, Sept 2014.

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  • Asymptotic Minimax Robust and Misspecified Lorden Quickest Change Detection For Dependent Stochastic Processes

    Molloy, T., & Ford, J. J. “Asymptotic Minimax Robust and Misspecified Lorden Quickest Change Detection For Dependent Stochastic Processes”, in Proc. of FUSION 2014. 


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  • Looming Aircraft Threats: Shape-based Passive Ranging of Aircraft from Monocular Vision

    Molloy, T. L., Ford, J & Mejias, L. Looming Aircraft Threats: Shape-based Passive Ranging of Aircraft from Monocular Vision, in Proc. of Australia Conference on Robotics and Automation (ACRA 2014), Dec. 2014. 


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  • Short-data Recursive HMM Parameter Estimation for Rapid Vision-based Aircraft Heading Estimation

    Molloy, T. L., & Ford, J, “Short-data Recursive HMM Parameter Estimation For Rapid Vision-based Aircraft Heading Estimation””, accepted to appear in Proc. of Australian Control Conference (AUCC 2014), Nov. 2014. 


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  • Condition-Invariant, Top-Down Visual Place Recognition

    Milford, M., Scheirer, W., Vig, E., Glover, A., Baumann, O., Mattingley, J., & Cox, D. "Condition-invariant, top-down visual place recognition", in Robotics and Automation (ICRA), 2014 IEEE International Conference on, pp.5571, 2014.

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  • Automated Sensory Data Alignment for Environmental and Epidermal Change Monitoring

    Milford, M., Firn, J., Beattie, J., Jacobson, A., Pepperell, E., Mason, E., Kimlin, M., & Dunbabin, M. (2014). Automated sensory data alignment for environmental and epidermal change monitoring. Australasian Conference on Robotics and Automation, ACRA, 02-04-December-2014.

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  • Featureless Visual Processing for SLAM in Changing Outdoor Environments

    Milford, M., & George, A. (2014) Featureless visual processing for SLAM in changing outdoor environments. In Field and Service Robotics: Results of the 8th International Conference [Springer Tracts in Advanced Robotics, Volume 92], Springer, Matsushima, Japan, pp. 569-583.

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  • Improving Global Multi-target Tracking with Local Updates

    Milan A., Gade R., Dick A., Moeslund T.B., Reid I. (2015) Improving Global Multi-target Tracking with Local Updates. In: Agapito L., Bronstein M., Rother C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science, vol 8927. Springer, Cham. https://doi.org/10.1007/978-3-319-16199-0_13

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  • Long-Term Exploration & Tours for Energy Constrained Robots with Online Proprioceptive Traversability Estimation

    Martin, S., & Corke, P., “Long-term exploration and tours for energy constrained robots with online pro ceptive traversability estimation,” in Proc. IEEE Int. Conf. Robotics and Automation, pp. 5778–5785, 2014

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  • Towards Training-Free Appearance-Based Localization: Probabilistic Models for Whole-Image Descriptors

    Lowry, S., Wyeth, G. F., & Milford, M. J. "Towards training-free appearance-based localization: probabilistic models for whole-image descriptors", in Robotics and Automation (ICRA), 2014 IEEE International Conference on, pp711-717, 2014.

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  • Unsupervised Online Learning of Condition-Invariant Images for Place Recognition

    Lowry, S., Wyeth, G. & Milford, M., "Unsupervised Online Learning of Condition-Invariant Images for Place Recognition", 2014 Australasian Conference on Robotics and Automation

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  • Transforming Morning to Afternoon using Linear Regression Techniques

    Lowry, S., Milford, M. J., Wyeth, & G. F. "Transforming morning to afternoon using linear regression techniques", in Robotics and Automation (ICRA), 2014 IEEE International Conference on, pp.3950-3955, 2014.

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  • Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors

    Liu, L., Shen, C., Wang, L., Van Den Hengel, A., & Wang, C. (2014). Encoding high dimensional local features by sparse coding based fisher vectors. Advances in Neural Information Processing Systems, 2(January), 1143–1151.

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  • Joint Semantic and Geometric Segmentation of Videos with a Stage Model

    Liu, B., He, X., & Gould, S., “Joint Semantic and Geometric Segmentation of Videos with a Stage Model”. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2014.

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  • Fast Supervised Hashing with Decision Trees for High-Dimensional Data

    Lin, G., Shen, C., Shi, Q., Van Den Hengel, A., & Suter, D. (2014). Fast supervised hashing with decision trees for high-dimensional data. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1971–1978. https://doi.org/10.1109/CVPR.2014.253

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  • Optimizing Ranking Measures for Compact Binary Code Learning

    Lin G., Shen C., Wu J. (2014) Optimizing Ranking Measures for Compact Binary Code Learning. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8691. Springer, Cham. https://doi.org/10.1007/978-3-319-10578-9_40

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  • Robust Online Visual Tracking with a Single Convolutional Neural Network

    Li H., Li Y., Porikli F. (2015) Robust Online Visual Tracking with a Single Convolutional Neural Network. In: Cremers D., Reid I., Saito H., Yang MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science, vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_13

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  • A Relaxation Method to Articulated Trajectory Reconstruction from Monocular Image Sequence

    Li, B., Dai, Y., He, M., & van den Hengel, A. "A relaxation method to articulated trajectory reconstruction from monocular image sequence," in Signal and Information Processing (ChinaSIP), 2014 IEEE China Summit & International Conference on, pp389-393, 2014.

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  • Text Recognition Approaches for Indoor Robotics: A Comparison

    Lam, O., Dayoub, F., Schulz, R., & Corke, P. (2014). Text recognition approaches for indoor robotics: a comparison. Proceedings of the 16th Australasian Conference on Robotics and Automation 2014.

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  • UPnP: An Optimal O(n) Solution to the Absolute Pose Problem with Universal Applicability

    Kneip L., Li H., Seo Y. (2014) UPnP: An Optimal O(n) Solution to the Absolute Pose Problem with Universal Applicability. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8689. Springer, Cham. https://doi.org/10.1007/978-3-319-10590-1_9

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  • Efficient Computation of Relative Pose for Multi-Camera Systems

    Kneip, L., & Li, H. (2014). Efficient computation of relative pose for multi-camera systems. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 446–453. https://doi.org/10.1109/CVPR.2014.64

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  • Null Space Clustering with Applications to Motion Segmentation and Face Clustering

    Ji, P., Zhong, Y., Li, H., & Salzmann, M. (2014). Null space clustering with applications to motion segmentation and face clustering. 2014 IEEE International Conference on Image Processing, ICIP 2014, 283–287. https://doi.org/10.1109/ICIP.2014.7025056

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  • Efficient Dense Subspace Clustering

    Ji, P., Salzmann, M., & Li, H. (2014). Efficient dense subspace clustering. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 461–468. https://doi.org/10.1109/WACV.2014.6836065

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  • Robust Motion Segmentation with Unknown Correspondences

    Ji P., Li H., Salzmann M., Dai Y. (2014) Robust Motion Segmentation with Unknown Correspondences. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8694. Springer, Cham. https://doi.org/10.1007/978-3-319-10599-4_14

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  • Optimizing Over Radial Kernels on Compact Manifolds

    Jayasumana, S., Hartley, R., Salzmann, M., Li, H., & Harandi, M. (2014). Optimizing over radial kernels on compact manifolds. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3802–3809. https://doi.org/10.1109/CVPR.2014.480

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  • Analysis of the Inverse Kinematics Problem for 3-DOF Axis-Symmetric Parallel Manipulators with Parasitic Motion

    Isaksson, M., Eriksson, A., & Nahavandi, S. (2014). Analysis of The inverse kinematics problem for 3-DOF axis-symmetric parallel manipulators with parasitic motion. Proceedings - IEEE International Conference on Robotics and Automation, 5736–5743. https://doi.org/10.1109/ICRA.2014.6907702

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  • An Exemplar-based CRF for Multi-instance Object Segmentation

    He, X., & Gould, S., “An Exemplar-based CRF for Multi-instance Object Segmentation”. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.

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  • Riemannian Manifolds, Kernels and Learning

    Hartley, R. "Keynote lecture 2: Riemannian manifolds, kernels and learning", in 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Aug. 2014.

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  • From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices

    Harandi M.T., Salzmann M., Hartley R. (2014) From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8690. Springer, Cham. https://doi.org/10.1007/978-3-319-10605-2_2

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  • Bregman Divergences for Infinite Dimensional Covariance Matrices

    Harandi, M., Salzmann, M., & Porikli, F. (2014). Bregman divergences for infinite dimensional covariance matrices. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1003–1010. https://doi.org/10.1109/CVPR.2014.132

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  • Automatic UAV Forced Landing Site Detection using Machine Learning

    Guo, F, Fookes, C., Denman, S, Mejias, L, & Sridharan, S., “Automatic UAV Emergency Landing Site Detection using Support Vector Machine”, IEEE International Conference on Digital Image Computing Techniques and Applications, 2014.

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  • Superpixel Graph Label Transfer with Learned Distance Metric

    Gould, S., Zhao, J, He, X., & Zhang, Y. “Superpixel Graph Label Transfer with Learned Distance Metric”. In Proceedings of the European Conference on Computer Vision (ECCV), 2014.

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  • Compressed sensing using hidden Markov models with application to vision based aircraft tracking

    Ford, J. J., Molloy, T. L., & Hall, J. L. “Compressed sensing using hidden Markov models with application to vision based aircraft tracking”, in Proc. of FUSION 2014. 


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  • Pseudoconvex Proximal Splitting for L∞ Problems in Multiview Geometry

    Eriksson, A., & Isaksson, M. (2014). Pseudoconvex proximal splitting for L∞problems in multiview geometry. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 4066–4073. https://doi.org/10.1109/CVPR.2014.518

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  • Vision Based Guidance for Robot Navigation in Agriculture

    English, A. R., Ross, P., Ball, D., & Corke, P. “Vision based guidance for robot navigation in agriculture Proc. IEEE Int. Conf. Robotics and Automation, pp. 1693–1698, 2014.

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  • Low rank or nuclear-norm minimization: Are we solving the right problem?

    Dai, Y., & Li, H. Rank Minimization or Nuclear-Norm Minimization: Are We Solving the Right Problem? DICTA 2014: 1-8

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  • Classification and Boosting with Multiple Collaborative Representations

    Chi, Y., & Porikli, F. (2014). Classification and boosting with multiple collaborative representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8), 1519–1531. https://doi.org/10.1109/TPAMI.2013.236

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  • Multi-Scale Bio-inspired Place Recognition

    Chen, Z., Jacobson, A., Erdem, U. M., Hasselmo, M. E., & Milford, M. (2014). Multi-scale bio-inspired place recognition. Proceedings - IEEE International Conference on Robotics and Automation, 1895–1901. https://doi.org/10.1109/ICRA.2014.6907109

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  • Convolutional Neural Network-based Place Recognition

    Chen, Z., Lam, O., Jacobson, A., & Milford, M. (2014). Convolutional neural network-based place recognition. Australasian Conference on Robotics and Automation, ACRA, 02-04-December-2014.

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  • Visual tracking via weakly supervised learning from multiple imperfect oracles

    Zhong, B., Yao, H., Chen, S., Ji, R., Chin, T. J., & Wang, H. (2014). Visual tracking via weakly supervised learning from multiple imperfect oracles. Pattern Recognition, 47(3), 1395–1410. https://doi.org/10.1016/j.patcog.2013.10.002

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  • As-Projective-As-Possible Image Stitching with Moving DLT

    Zaragoza, J., Chin, T. J., Tran, Q. H., Brown, M. S., & Suter, D. (2014). As-projective-as-possible image stitching with moving DLT. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7), 1285–1298. https://doi.org/10.1109/TPAMI.2013.247

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  • Multi-subregion based correlation filter bank for robust face recognition

    Yan, Y., Wang, H., & Suter, D. (2014). Multi-subregion based correlation filter bank for robust face recognition. Pattern Recognition, 47(11), 3487–3501. https://doi.org/10.1016/j.patcog.2014.05.004

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  • Efficient Semidefinite Spectral Clustering via Lagrange Duality

    Yan, Y., Shen, C., & Wang, H. (2014). Efficient semidefinite spectral clustering via lagrange duality. IEEE Transactions on Image Processing, 23(8), 3522–3534. https://doi.org/10.1109/TIP.2014.2329453

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  • Vision-Based Cooperative Pose Estimation for Localization in Multi-Robot Systems Equipped with RGB-D Cameras

    Wang, X., Şekercioğlu, Y., & Drummond, T. (2014). Vision-Based Cooperative Pose Estimation for Localization in Multi-Robot Systems Equipped with RGB-D Cameras. Robotics, 4(1), 1–22. https://doi.org/10.3390/robotics4010001

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  • A Hierarchical Word-Merging Algorithm with Class Separability Measure

    Wang, L., Zhou, L., Shen, C., Liu, L., & Liu, H. (2014). A hierarchical word-merging algorithm with class separability measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), 417–435. https://doi.org/10.1109/TPAMI.2013.160

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  • Sampson Distance Based Joint Estimation of Multiple Homographies with Uncalibrated Cameras

    Szpak, Z. L., Chojnacki, W., Eriksson, A., & Van Den Hengel, A. (2014). Sampson distance based joint estimation of multiple homographies with uncalibrated cameras. Computer Vision and Image Understanding, 125, 200–213. https://doi.org/10.1016/j.cviu.2014.04.008

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  • Guaranteed Ellipse Fitting with a Confidence Region and an Uncertainty Measure for Centre, Axes, and Orientation

    Szpak, Z. L., Chojnacki, W., & van den Hengel, A. (2015). Guaranteed Ellipse Fitting with a Confidence Region and an Uncertainty Measure for Centre, Axes, and Orientation. Journal of Mathematical Imaging and Vision, 52(2), 173–199. https://doi.org/10.1007/s10851-014-0536-x

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  • StructBoost: Boosting Methods for Predicting Structured Output Variables

    Shen, C., Lin, G., & Van Den Hengel, A. (2014). StructBoost: Boosting Methods for Predicting Structured Output Variables. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10), 2089–2103. https://doi.org/10.1109/TPAMI.2014.2315792

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  • The Random Cluster Model for Robust Geometric Fitting

    Pham, T. T., Chin, T. J., Yu, J., & Suter, D. (2014). The random cluster model for robust geometric fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8), 1658–1671. https://doi.org/10.1109/TPAMI.2013.2296310

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  • Interacting Geometric Priors For Robust Multimodel Fitting

    Pham, T. T., Chin, T. J., Schindler, K., & Suter, D. (2014). Interacting geometric priors for robust multimodel fitting. IEEE Transactions on Image Processing, 23(10), 4601–4610. https://doi.org/10.1109/TIP.2014.2346025

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  • RandomBoost: Simplified Multi-class Boosting through Randomization

    Paisitkriangkrai, S., Shen, C., Shi, Q., & Van Den Hengel, A. (2014). RandomBoost: Simplified multiclass boosting through randomization. IEEE Transactions on Neural Networks and Learning Systems, 25(4), 764–779. https://doi.org/10.1109/TNNLS.2013.2281214

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  • Asymmetric Pruning For Learning Cascade Detectors

    Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). Asymmetric pruning for learning cascade detectors. IEEE Transactions on Multimedia, 16(5), 1254–1267. https://doi.org/10.1109/TMM.2014.2308723

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  • A Scalable Stagewise Approach to Large-Margin Multiclass Loss-Based Boosting

    Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). A scalable stagewise approach to large-margin multiclass loss-based boosting. IEEE Transactions on Neural Networks and Learning Systems, 25(5), 1002–1013. https://doi.org/10.1109/TNNLS.2013.2282369

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  • Large-margin Learning of Compact Binary Image Encodings

    Paisitkriangkrai, S., Shen, C., & Hengel, A. Van Den. (2014). Large-margin learning of compact binary image encodings. IEEE Transactions on Image Processing, 23(9), 4041–4054. https://doi.org/10.1109/TIP.2014.2337759

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  • Compass Rose: A Rotational Robust Signature for Optical Flow

    Niu, Y., Dick, A., & Brooks, M. (2014). Compass rose: A rotational robust signature for optical flow computation. IEEE Transactions on Circuits and Systems for Video Technology, 24(1), 63–73. https://doi.org/10.1109/TCSVT.2013.2276854

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  • Vision‐based Simultaneous Localization and Mapping in Changing Outdoor Environments

    Milford, M., Vig, E., Scheirer, W., & Cox, D. (2014). Vision-based Simultaneous Localization and Mapping in Changing Outdoor Environments. Journal of Field Robotics, 31(5), 780–802. https://doi.org/10.1002/rob.21532

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  • Principles of goal-directed spatial robot navigation in biomimetic models

    Milford, M., & Schulz, R. (2014). Principles of goal-directed spatial robot navigation in biomimetic models. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1655). https://doi.org/10.1098/rstb.2013.0484

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  • Visual Predictive Control of Spiral Motion

    Mcfadyen, A., Corke, P., & Mejias, L. (2014). Visual predictive control of spiral motion. IEEE Transactions on Robotics, 30(6), 1441–1454. https://doi.org/10.1109/TRO.2014.2361425

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  • Multiple kernel clustering based on centered kernel alignment

    Lu, Y., Wang, L., Lu, J., Yang, J., & Shen, C. (2014). Multiple kernel clustering based on centered kernel alignment. Pattern Recognition, 47(11), 3656–3664. https://doi.org/10.1016/j.patcog.2014.05.005

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  • Optimal Camera Planning Under Versatile User Constraints in Multi-Camera Image Processing Systems

    Liu, J., Sridharan, S., Fookes, C., & Wark, T. (2014). Optimal camera planning under versatile user constraints in multi-camera image processing systems. IEEE Transactions on Image Processing, 23(1), 171–184. https://doi.org/10.1109/TIP.2013.2287606

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  • Multiple Kernel Learning in the Primal for Multi-modal Alzheimer’s Disease Classification

    Liu, F., Zhou, L., Shen, C., & Yin, J. (2014). Multiple kernel learning in the primal for multimodal alzheimer’s disease classification. IEEE Journal of Biomedical and Health Informatics, 18(3), 984–990. https://doi.org/10.1109/JBHI.2013.2285378

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  • Characterness: An Indicator of Text in the Wild

    Li, Y., Jia, W., Shen, C., & Van Den Hengel, A. (2014). Characterness: An indicator of text in the wild. IEEE Transactions on Image Processing, 23(4), 1666–1677. https://doi.org/10.1109/TIP.2014.2302896

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  • Context-Aware Hypergraph Construction for Robust Spectral Clustering

    Li, X., Hu, W., Shen, C., Dick, A., & Zhang, Z. (2014). Context-aware hypergraph construction for robust spectral clustering. IEEE Transactions on Knowledge and Data Engineering, 26(10), 2588–2597. https://doi.org/10.1109/TKDE.2013.126

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  • Recognizing Gaits Across Views Through Correlated Motion Co-Clustering

    Kusakunniran, W., Wu, Q., Zhang, J., Li, H., & Wang, L. (2014). Recognizing gaits across views through correlated motion co-clustering. IEEE Transactions on Image Processing, 23(2), 696–709. https://doi.org/10.1109/TIP.2013.2294552

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  • Projective Reconstruction

    Hartley, R. "Projective Reconstruction," in Computer Vision: A Reference Guide, 2014, pp640-651

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  • Scene Understanding by Labeling Pixels

    Gould, S., & He, X. (2014). Scene understanding by labeling pixels. Communications of the ACM, 57(11), 68–77. https://doi.org/10.1145/2629637

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  • Ranking consistency for image matching and object retrieval

    Chen, Y., Li, X., Dick, A., & Hill, R. (2014). Ranking consistency for image matching and object retrieval. Pattern Recognition, 47(3), 1349–1360. https://doi.org/10.1016/j.patcog.2013.09.011

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  • Context Based Re-ranking for Object Retrieval

    Chen, Y., Dick, A., Li, X. Context Based Re-ranking for Object Retrieval. Proc. Asian Conf. Computer Vision, 2014.

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  • Artistic Image Analysis using the Composition of Human Figures

    Chen Q., Carneiro G. (2015) Artistic Image Analysis Using the Composition of Human Figures. In: Agapito L., Bronstein M., Rother C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science, vol 8925. Springer, Cham. https://doi.org/10.1007/978-3-319-16178-5_8

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  • A fast, modular scene understanding system using context-aware object detection

    Cadena, C., Dick, A., & Reid, I.. A Fast Modular Scene Understanding System using Context-Aware Object Detection, accepted for International Conference on Robotics and Automation, 2015

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  • Biologically inspired slam using wi-fi

    Berkvens, R., Jacobson, A., Milford, M., Peremans, H., & Weyn, M. (2014). Biologically inspired SLAM using Wi-Fi. IEEE International Conference on Intelligent Robots and Systems, 1804–1811. https://doi.org/10.1109/IROS.2014.6942799

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  • Fully automated non-rigid segmentation with distance regularized level set evolution initialized and constrained by deep-structured inference

    Ngo, T. A., & Carneiro, G. (2014). Fully automated non-rigid segmentation with distance regularized level set evolution initialized and constrained by deep-structured inference. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3118–3125. https://doi.org/10.1109/CVPR.2014.399

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  • Local inter-session variability modelling for object classification

    Anantharajah, K., Ge, Z. Y., McCool, C., Denman, S., Fookes, C., Corke, P., Tjondronegoro, D., & Sridharan, S. (2014). Local inter-session variability modelling for object classification. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 309–316. https://doi.org/10.1109/WACV.2014.6836084

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  • Semidefinite Programming

    Shen, C. and van den Hengel, A. 2014. "Semidefinite Programming", in Computer Vision: A Reference Guide, Springer US, pp. 717-719

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  • Large-Scale Camera Topology Mapping: Application to Re-identification

    Dick, A., Van Den Hengel, A., & Detmold, H. (2014). Large-scale camera topology mapping: Application to re-identification. In Advances in Computer Vision and Pattern Recognition (Vol. 56, pp. 391–411). Springer-Verlag London Ltd. https://doi.org/10.1007/978-1-4471-6296-4_19

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  • Iteratively Reweighted Graph Cut for Multi-label MRFs with Nonconvex Priors

    *Ajanthan, T., Hartley, R., Salzmann, M., & Li, H. Iteratively
    Reweighted Graph Cut for Multi-label MRFs with Nonconvex
    Priors. arXiv preprint arXiv:1411.6340, 24 Nov
    2014.

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  • Globally Optimal Inlier Set Maximization With Unknown Rotation and Focal Length

    Bazin, J. C., Seo, Y., Hartley, R., & Pollefeys, M. (2014). Globally optimal inlier set maximization with unknown rotation and focal length. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8690 LNCS(PART 2), 803–817. https://doi.org/10.1007/978-3-319-10605-2_52

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Book Chapters

  • Tracking and Segmentation of the Endocardium of the Left Ventricle in 2D Ultrasound using Deep Learning Architectures and Monte Carlo Sampling

    Nascimento, J. C., Carneiro, G., & Freitas, A. (2014). Tracking and Segmentation of the Endocardium of the Left Ventricle in 2D Ultrasound using Deep Learning Architectures and Monte Carlo Sampling.

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  • Projecto Printart: Sistema Computatcional Para Apoio Ao Estudo da Azulejaria Portuguesa

    Carneiro, G. & Lazaro, D. Projecto PRINTART: Sistema computacional para apoio ao estudo da azulejaria portuguesa. Chapter in Book “A Herança de Santos Simões Novas Perspectivas para o Estudo da Azulejaria e da Cerâmica” (in Portuguese). ISBN: 978-989-689-400-9. 2014.

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  • Semidefinite Programming

    Shen, C. and van den Hengel, A. 2014. "Semidefinite Programming", in Computer Vision: A Reference Guide, Springer US, pp. 717-719

    View More

  • Large-Scale Camera Topology Mapping: Application to Re-identification

    Dick, A., Van Den Hengel, A., & Detmold, H. (2014). Large-scale camera topology mapping: Application to re-identification. In Advances in Computer Vision and Pattern Recognition (Vol. 56, pp. 391–411). Springer-Verlag London Ltd. https://doi.org/10.1007/978-1-4471-6296-4_19

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Journal Articles

  • Fast Approximate L ∞ Minimization: Speeding Up Robust Regression

    Shen, F., Shen, C., Hill, R., Van Den Hengel, A., & Tang, Z. (2014). Fast approximate L∞ minimization: Speeding up robust regression. Computational Statistics and Data Analysis, 77, 25–37. https://doi.org/10.1016/j.csda.2014.02.018

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  • An Efficient Dual Approach to Distance Metric Learning

    Shen, C., Kim, J., Liu, F., Wang, L., & Van Den Hengel, A. (2014). Efficient dual approach to distance metric learning. IEEE Transactions on Neural Networks and Learning Systems, 25(2), 394–406. https://doi.org/10.1109/TNNLS.2013.2275170

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  • Enhancing Moving Objects in Light Field Videos Using 5-D IIR Adaptive Depth-Velocity Filters

    Edussooriya, C., Dansereau, D., Bruton, L., & Agathoklis, P. 2015. "Five-Dimensional (5-D) Depth-Velocity Filtering for Enhancing Moving Objects in Light Field Videos." IEEE Transactions on Signal Processing (TSP).

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  • Classification and Boosting with Multiple Collaborative Representations

    Chi, Y., & Porikli, F. (2014). Classification and boosting with multiple collaborative representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8), 1519–1531. https://doi.org/10.1109/TPAMI.2013.236

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  • Visual tracking via weakly supervised learning from multiple imperfect oracles

    Zhong, B., Yao, H., Chen, S., Ji, R., Chin, T. J., & Wang, H. (2014). Visual tracking via weakly supervised learning from multiple imperfect oracles. Pattern Recognition, 47(3), 1395–1410. https://doi.org/10.1016/j.patcog.2013.10.002

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  • As-Projective-As-Possible Image Stitching with Moving DLT

    Zaragoza, J., Chin, T. J., Tran, Q. H., Brown, M. S., & Suter, D. (2014). As-projective-as-possible image stitching with moving DLT. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7), 1285–1298. https://doi.org/10.1109/TPAMI.2013.247

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  • Multi-subregion based correlation filter bank for robust face recognition

    Yan, Y., Wang, H., & Suter, D. (2014). Multi-subregion based correlation filter bank for robust face recognition. Pattern Recognition, 47(11), 3487–3501. https://doi.org/10.1016/j.patcog.2014.05.004

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  • Efficient Semidefinite Spectral Clustering via Lagrange Duality

    Yan, Y., Shen, C., & Wang, H. (2014). Efficient semidefinite spectral clustering via lagrange duality. IEEE Transactions on Image Processing, 23(8), 3522–3534. https://doi.org/10.1109/TIP.2014.2329453

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  • Vision-Based Cooperative Pose Estimation for Localization in Multi-Robot Systems Equipped with RGB-D Cameras

    Wang, X., Şekercioğlu, Y., & Drummond, T. (2014). Vision-Based Cooperative Pose Estimation for Localization in Multi-Robot Systems Equipped with RGB-D Cameras. Robotics, 4(1), 1–22. https://doi.org/10.3390/robotics4010001

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  • A Hierarchical Word-Merging Algorithm with Class Separability Measure

    Wang, L., Zhou, L., Shen, C., Liu, L., & Liu, H. (2014). A hierarchical word-merging algorithm with class separability measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), 417–435. https://doi.org/10.1109/TPAMI.2013.160

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  • Sampson Distance Based Joint Estimation of Multiple Homographies with Uncalibrated Cameras

    Szpak, Z. L., Chojnacki, W., Eriksson, A., & Van Den Hengel, A. (2014). Sampson distance based joint estimation of multiple homographies with uncalibrated cameras. Computer Vision and Image Understanding, 125, 200–213. https://doi.org/10.1016/j.cviu.2014.04.008

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  • Guaranteed Ellipse Fitting with a Confidence Region and an Uncertainty Measure for Centre, Axes, and Orientation

    Szpak, Z. L., Chojnacki, W., & van den Hengel, A. (2015). Guaranteed Ellipse Fitting with a Confidence Region and an Uncertainty Measure for Centre, Axes, and Orientation. Journal of Mathematical Imaging and Vision, 52(2), 173–199. https://doi.org/10.1007/s10851-014-0536-x

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  • StructBoost: Boosting Methods for Predicting Structured Output Variables

    Shen, C., Lin, G., & Van Den Hengel, A. (2014). StructBoost: Boosting Methods for Predicting Structured Output Variables. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10), 2089–2103. https://doi.org/10.1109/TPAMI.2014.2315792

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  • The Random Cluster Model for Robust Geometric Fitting

    Pham, T. T., Chin, T. J., Yu, J., & Suter, D. (2014). The random cluster model for robust geometric fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8), 1658–1671. https://doi.org/10.1109/TPAMI.2013.2296310

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  • Interacting Geometric Priors For Robust Multimodel Fitting

    Pham, T. T., Chin, T. J., Schindler, K., & Suter, D. (2014). Interacting geometric priors for robust multimodel fitting. IEEE Transactions on Image Processing, 23(10), 4601–4610. https://doi.org/10.1109/TIP.2014.2346025

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  • RandomBoost: Simplified Multi-class Boosting through Randomization

    Paisitkriangkrai, S., Shen, C., Shi, Q., & Van Den Hengel, A. (2014). RandomBoost: Simplified multiclass boosting through randomization. IEEE Transactions on Neural Networks and Learning Systems, 25(4), 764–779. https://doi.org/10.1109/TNNLS.2013.2281214

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  • Asymmetric Pruning For Learning Cascade Detectors

    Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). Asymmetric pruning for learning cascade detectors. IEEE Transactions on Multimedia, 16(5), 1254–1267. https://doi.org/10.1109/TMM.2014.2308723

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  • A Scalable Stagewise Approach to Large-Margin Multiclass Loss-Based Boosting

    Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). A scalable stagewise approach to large-margin multiclass loss-based boosting. IEEE Transactions on Neural Networks and Learning Systems, 25(5), 1002–1013. https://doi.org/10.1109/TNNLS.2013.2282369

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  • Large-margin Learning of Compact Binary Image Encodings

    Paisitkriangkrai, S., Shen, C., & Hengel, A. Van Den. (2014). Large-margin learning of compact binary image encodings. IEEE Transactions on Image Processing, 23(9), 4041–4054. https://doi.org/10.1109/TIP.2014.2337759

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  • Compass Rose: A Rotational Robust Signature for Optical Flow

    Niu, Y., Dick, A., & Brooks, M. (2014). Compass rose: A rotational robust signature for optical flow computation. IEEE Transactions on Circuits and Systems for Video Technology, 24(1), 63–73. https://doi.org/10.1109/TCSVT.2013.2276854

    View More

  • Vision‐based Simultaneous Localization and Mapping in Changing Outdoor Environments

    Milford, M., Vig, E., Scheirer, W., & Cox, D. (2014). Vision-based Simultaneous Localization and Mapping in Changing Outdoor Environments. Journal of Field Robotics, 31(5), 780–802. https://doi.org/10.1002/rob.21532

    View More

  • Principles of goal-directed spatial robot navigation in biomimetic models

    Milford, M., & Schulz, R. (2014). Principles of goal-directed spatial robot navigation in biomimetic models. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1655). https://doi.org/10.1098/rstb.2013.0484

    View More

  • Visual Predictive Control of Spiral Motion

    Mcfadyen, A., Corke, P., & Mejias, L. (2014). Visual predictive control of spiral motion. IEEE Transactions on Robotics, 30(6), 1441–1454. https://doi.org/10.1109/TRO.2014.2361425

    View More

  • Multiple kernel clustering based on centered kernel alignment

    Lu, Y., Wang, L., Lu, J., Yang, J., & Shen, C. (2014). Multiple kernel clustering based on centered kernel alignment. Pattern Recognition, 47(11), 3656–3664. https://doi.org/10.1016/j.patcog.2014.05.005

    View More

  • Optimal Camera Planning Under Versatile User Constraints in Multi-Camera Image Processing Systems

    Liu, J., Sridharan, S., Fookes, C., & Wark, T. (2014). Optimal camera planning under versatile user constraints in multi-camera image processing systems. IEEE Transactions on Image Processing, 23(1), 171–184. https://doi.org/10.1109/TIP.2013.2287606

    View More

  • Multiple Kernel Learning in the Primal for Multi-modal Alzheimer’s Disease Classification

    Liu, F., Zhou, L., Shen, C., & Yin, J. (2014). Multiple kernel learning in the primal for multimodal alzheimer’s disease classification. IEEE Journal of Biomedical and Health Informatics, 18(3), 984–990. https://doi.org/10.1109/JBHI.2013.2285378

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  • Characterness: An Indicator of Text in the Wild

    Li, Y., Jia, W., Shen, C., & Van Den Hengel, A. (2014). Characterness: An indicator of text in the wild. IEEE Transactions on Image Processing, 23(4), 1666–1677. https://doi.org/10.1109/TIP.2014.2302896

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  • Context-Aware Hypergraph Construction for Robust Spectral Clustering

    Li, X., Hu, W., Shen, C., Dick, A., & Zhang, Z. (2014). Context-aware hypergraph construction for robust spectral clustering. IEEE Transactions on Knowledge and Data Engineering, 26(10), 2588–2597. https://doi.org/10.1109/TKDE.2013.126

    View More

  • Recognizing Gaits Across Views Through Correlated Motion Co-Clustering

    Kusakunniran, W., Wu, Q., Zhang, J., Li, H., & Wang, L. (2014). Recognizing gaits across views through correlated motion co-clustering. IEEE Transactions on Image Processing, 23(2), 696–709. https://doi.org/10.1109/TIP.2013.2294552

    View More

  • Projective Reconstruction

    Hartley, R. "Projective Reconstruction," in Computer Vision: A Reference Guide, 2014, pp640-651

    View More

  • Scene Understanding by Labeling Pixels

    Gould, S., & He, X. (2014). Scene understanding by labeling pixels. Communications of the ACM, 57(11), 68–77. https://doi.org/10.1145/2629637

    View More

  • Ranking consistency for image matching and object retrieval

    Chen, Y., Li, X., Dick, A., & Hill, R. (2014). Ranking consistency for image matching and object retrieval. Pattern Recognition, 47(3), 1349–1360. https://doi.org/10.1016/j.patcog.2013.09.011

    View More

  • Iteratively Reweighted Graph Cut for Multi-label MRFs with Nonconvex Priors

    *Ajanthan, T., Hartley, R., Salzmann, M., & Li, H. Iteratively
    Reweighted Graph Cut for Multi-label MRFs with Nonconvex
    Priors. arXiv preprint arXiv:1411.6340, 24 Nov
    2014.

    View More

  • Globally Optimal Inlier Set Maximization With Unknown Rotation and Focal Length

    Bazin, J. C., Seo, Y., Hartley, R., & Pollefeys, M. (2014). Globally optimal inlier set maximization with unknown rotation and focal length. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8690 LNCS(PART 2), 803–817. https://doi.org/10.1007/978-3-319-10605-2_52

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Conference Papers

  • Expanding the Family of Grassmannian Kernels: An Embedding Perspective

    Harandi, M. T., Salzmann, M., Jayasumana, S., Hartley, R., & Li, H. 2014. Expanding the Family of Grassmannian Kernels: An Embedding Perspective. Computer Vision-ECCV 2014. Springer International Publishing. pp408-423

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  • DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking

    Li, H., Li, Y., & Porikli, F. (2014). DeepTrack: Learning discriminative feature representations by Convolutional Neural Networks for visual tracking. BMVC 2014 - Proceedings of the British Machine Vision Conference 2014. https://doi.org/10.5244/c.28.56

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  • Optimal Essential Matrix Estimation via Inlier-Set Maximization

    Yang, J., Li, H., & Jia, Y. Optimal Essential Matrix Estimation via Inlier-Set Maximization. ECCV (1) 2014: 111-126

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  • CDnet 2014: An Expanded Change Detection Benchmark Dataset

    Wang, Y., Jodoin, P. M., Porikli, F., Konrad, J., Benezeth, Y., & Ishwar, P. (2014). CDnet 2014: An expanded change detection benchmark dataset. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 393–400. https://doi.org/10.1109/CVPRW.2014.126

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  • Counting People by Clustering Person Detector Outputs

    Topkaya, I. S., Erdogan, H., & Porikli, F. (2014). Counting people by clustering person detector outputs. 11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014, 313–318. https://doi.org/10.1109/AVSS.2014.6918687

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  • Sparse Dictionaries for Semantic Segmentation

    Tao L., Porikli F., Vidal R. (2014) Sparse Dictionaries for Semantic Segmentation. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693. Springer, Cham. https://doi.org/10.1007/978-3-319-10602-1_36

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  • Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction

    Sünderhauf, N., McCool, C., Upcroft, B., Perez., T. (2014). Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction. Proc. of ImageCLEF Workshop, International Conference of the CLEF Initiative, Sheffield, UK.

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  • Recycled Linear Classifiers for Multiclass Classification

    Soni, A., Haupt, J., & Porikli, F. (2014). Recycled linear classifiers for multiclass classification. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2957–2961. https://doi.org/10.1109/ICASSP.2014.6854142

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  • Determining Interacting Objects in Human-Centric Activities via Qualitative Spatio-Temporal Reasoning

    Sokeh H.S., Gould S., Renz J. (2015) Determining Interacting Objects in Human-Centric Activities via Qualitative Spatio-Temporal Reasoning. In: Cremers D., Reid I., Saito H., Yang MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science, vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_36

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  • Super-Resolving Noisy Images

    Singh, A., Porikli, F., & Ahuja, N. (2014). Super-resolving noisy images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2846–2853. https://doi.org/10.1109/CVPR.2014.364

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  • Robust Orthonormal Subspace Learning: Efficient Recovery of Corrupted Low-rank Matrices

    Shu, X., Porikli, F., & Ahuja, N. (2014). Robust orthonormal subspace learning: Efficient recovery of corrupted low-rank matrices. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3874–3881. https://doi.org/10.1109/CVPR.2014.495

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  • Object Tracking via Non-Euclidean Geometry: A Grassmann Approach

    Shirazi, S., Harandi, M. T., Lovell, B. C., & Sanderson, C. (2014). Object tracking via non-Euclidean geometry: A Grassmann approach. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 901–908. https://doi.org/10.1109/WACV.2014.6836008

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  • Hybrid Inference Optimization for Robust Pose Graph Estimation

    Segal, A. & Reid, I. Hybrid Inference Optimization for Robust Pose Graph Estimation, International Conference on Intelligent Robots and Systems 2014

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  • Inspection of Pole-Like Structures Using a Vision-Controlled VTOL UAV and Shared Autonomy

    Sa, I., Hrabar, S., & Corke, P. (2014). Inspection of pole-like structures using a vision-controlled VTOL UAV and shared autonomy. IEEE International Conference on Intelligent Robots and Systems, 4819–4826. https://doi.org/10.1109/IROS.2014.6943247

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  • Novelty-Based Visual Obstacle Detection in Agriculture

    Ross, P., English, A. R., Ball, D., Upcroft, B, Wyeth, G., & Corke, P., “Novelty-based visual obstacle dete in agriculture,” in Proc. IEEE Int. Conf. Robotics and Automation, pp. 1699–1705, 2014.

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  • A Method to Quantify a Descriptor’s Illumination Variance

    Ross, P., English, A, Ball, D., & Corke, P., “A method to quantify a descriptor’s illumination variance Australian Conference on Robotics and Automation, 2014.

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  • 3D Tracking of Multiple Objects with Identical Appearance using RGB-D Input

    Ren, C., Prisacariu, V., Murray, D., & Reid, I. 3D Tracking of Multiple Objects with Identical Appearance using RGB-D Input, 3DV, Dec 2014

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  • All-Environment Visual Place Recognition with SMART

    Pepperell, E., Corke, P., & Milford, M. “All-environment visual place recognition with smart,” in Proc. I Int. Conf. Robotics and Automation, pp. 1612–1618, 2014.

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  • Towards Vision-Based Pose- and Condition-Invariant Place Recognition along Routes

    Pepperell, E., Corke, P., & Milford, M. "Towards Vision-Based Pose- and Condition-Invariant Place Recognition along Routes", 2014 Australasian Conference on Robotics and Automation

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  • Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features

    Paisitkriangkrai S., Shen C., van den Hengel A. (2014) Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8692. Springer, Cham. https://doi.org/10.1007/978-3-319-10593-2_36

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  • Local Refinement for Stereo Regularization

    Olsson, C., Ulen, J., & Eriksson, A. (2014). Local refinement for stereo regularization. Proceedings - International Conference on Pattern Recognition, 4056–4061. https://doi.org/10.1109/ICPR.2014.695

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  • Empirical Modelling of Rolling Shutter Effect

    O’Sullivan, L., & Corke, P. (2014). Empirical modelling of rolling shutter effect. Proceedings - IEEE International Conference on Robotics and Automation, 2132–2137. https://doi.org/10.1109/ICRA.2014.6907152

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  • Detecting 3D Geometric Boundaries of Indoor Scenes Under Varying Lighting

    Ni, J., Marks, T. K., Tuzel, O., & Porikli, F. (2014). Detecting 3D geometric boundaries of indoor scenes under varying lighting. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 1–8. https://doi.org/10.1109/WACV.2014.6836125

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  • On Projective Reconstruction In Arbitrary Dimensions

    Nasihatkon, B., Hartley, R., & Trumpf, J. (2014). On projective reconstruction in arbitrary dimensions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 477–484. https://doi.org/10.1109/CVPR.2014.68

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    Chen Q., Carneiro G. (2015) Artistic Image Analysis Using the Composition of Human Figures. In: Agapito L., Bronstein M., Rother C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science, vol 8925. Springer, Cham. https://doi.org/10.1007/978-3-319-16178-5_8

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