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2018 Journal Articles [40]

Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters

Zhu, G., Porikli, F., & Li, H. (2018). Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters. IEEE Transactions on Circuits and Systems for Video Technology, 28(2), 314–326. http://doi.org/10.1109/TCSVT.2016.2615518

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Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study

Yan, Y., Tan, M., Tsang, I., Yang, Y., Shi, Q., & Zhang, C. (2018). Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study. IEEE Transactions on Knowledge and Data Engineering, 1–1. https://doi.org/10.1109/TKDE.2018.2882197

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Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables

Varamin, A. A., Abbasnejad, E., Shi, Q., Ranasinghe, D. C., & Rezatofighi, H. (2018). Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables (Vol. 18). Retrieved from https://doi.org/10.475/123_4

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Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine

Saha, S. K., Fernando, B., Cuadros, J., Xiao, D., & Kanagasingam, Y. (2018). Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine. Journal of Digital Imaging, 31(6), 869–878. http://doi.org/10.1007/s10278-018-0084-9

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Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing

Liu, W., Zhang, P., Chen, X., Shen, C., Huang, X., & Yang, J. (2018). Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. http://doi.org/10.1109/TCSVT.2018.2890202

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Robust and Efficient Relative Pose With a Multi-Camera System for Autonomous Driving in Highly Dynamic Environments

Liu, L., Li, H., Dai, Y., & Pan, Q. (2018). Robust and Efficient Relative Pose With a Multi-Camera System for Autonomous Driving in Highly Dynamic Environments. IEEE Transactions on Intelligent Transportation Systems, 19(8), 2432–2444. http://doi.org/10.1109/TITS.2017.2749409

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Reading car license plates using deep neural networks

Li, H., Wang, P., You, M., & Shen, C. (2018). Reading car license plates using deep neural networks. Image and Vision Computing, 72, 14–23. http://doi.org/10.1016/J.IMAVIS.2018.02.002

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Exploring Context with Deep Structured Models for Semantic Segmentation

Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2018). Exploring Context with Deep Structured Models for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1352–1366. http://doi.org/10.1109/TPAMI.2017.2708714

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Drones count wildlife more accurately and precisely than humans

Hodgson, J. C., Mott, R., Baylis, S. M., Pham, T. T., Wotherspoon, S., Kilpatrick, A. D., Ramesh, R.S., Reid, I., Terauds, A., & Koh, L. P. (2018). Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution, 9(5), 1160–1167. http://doi.org/10.1111/2041-210X.12974

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Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks.

Han, X., Lu, J., Zhao, C., You, S., & Li, H. (2018). Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks. IEEE Signal Processing Letters, 25(4), 551–555. http://doi.org/10.1109/LSP.2018.2809685

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Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression

Guo, G., Wang, H., Shen, C., Yan, Y., & Liao, H.-Y. M. (2018). Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression. IEEE Transactions on Multimedia, 20(8), 2073–2085. http://doi.org/10.1109/TMM.2018.2794262

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Visual Permutation Learning

Santa Cruz, R., Fernando, B., Cherian, A., & Gould, S. (2018). Visual Permutation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (Vol. PP). IEEE. https://doi.org/10.1109/TPAMI.2018.2873701 *Early Access

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Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation

Campbell, D. J., Petersson, L., Kneip, L., & Li, H. (2018). Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. http://doi.org/10.1109/TPAMI.2018.2848650 *In Press

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Action Recognition with Dynamic Image Networks

Bilen, H., Fernando, B., Gavves, E., & Vedaldi, A. (2018). Action Recognition with Dynamic Image Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2799–2813. http://doi.org/10.1109/TPAMI.2017.2769085

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Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting

Wang, H., guobao, xiao, Yan, Y., & Suter, D. (2018). Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2018.2803173 *In Press

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Semantics-Aware Visual Object Tracking

Yao, R., Lin, G., Shen, C., Zhang, Y., & Shi, Q. (2018). Semantics-Aware Visual Object Tracking. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. https://doi.org/10.1109/TCSVT.2018.2848358 *In Press

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Learning Context Flexible Attention Model for Long-Term Visual Place Recognition

Chen, Z., Liu, L., Sa, I., Ge, Z., & Chli, M. (2018). Learning Context Flexible Attention Model for Long-Term Visual Place Recognition. IEEE Robotics and Automation Letters, 3(4), 4015–4022. http://doi.org/10.1109/LRA.2018.2859916

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Unsupervised Domain Adaptation Using Robust Class-Wise Matching

Zhang, L., Wang, P., Wei, W., Lu, H., Shen, C., van den Hengel, A., & Zhang, Y. (2018). Unsupervised Domain Adaptation Using Robust Class-Wise Matching. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/TCSVT.2018.2842206 *In Press

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QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM

Nicholson, L., Milford, M., & Sunderhauf, N. (2019). QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM. IEEE Robotics and Automation Letters, 4(1), 1–8. http://doi.org/10.1109/LRA.2018.2866205

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Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks

Wang, X., Şekercioğlu, Y., Drummond, T., Frémont, V., Natalizio, E., & Fantoni, I. (2018). Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks. Sensors, 18(8), 2430. http://doi.org/10.3390/s18082430

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Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks

Liao, Z., Drummond, T., Reid, I., & Carneiro, G. (2018). Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. http://doi.org/10.1109/TPAMI.2018.2876413

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A review of deep learning in the study of materials degradation

Nash, W., Drummond, T., & Birbilis, N. (2018). A review of deep learning in the study of materials degradation. Npj Materials Degradation, 2(1), 37. http://doi.org/10.1038/s41529-018-0058-x

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An Extended Filtered Channel Framework for Pedestrian Detection

You, M., Zhang, Y., Shen, C., & Zhang, X. (2018). An Extended Filtered Channel Framework for Pedestrian Detection. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1640–1651. https://doi.org/10.1109/TITS.2018.2807199

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An Embarrassingly Simple Approach to Visual Domain Adaptation

Lu, H., Shen, C., Cao, Z., Xiao, Y., & van den Hengel, A. (2018). An Embarrassingly Simple Approach to Visual Domain Adaptation. IEEE Transactions on Image Processing, 27(7), 3403–3417. https://doi.org/10.1109/TIP.2018.2819503

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Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction

Zhang, L., Wei, W., Zhang, Y., Shen, C., van den Hengel, A., & Shi, Q. (2018). Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction. International Journal of Computer Vision, 126(8), 797–821. https://doi.org/10.1007/s11263-018-1080-8

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Multi-label learning based deep transfer neural network for facial attribute classification

Zhuang, N., Yan, Y., Chen, S., Wang, H., & Shen, C. (2018). Multi-label learning based deep transfer neural network for facial attribute classification. Pattern Recognition, 80, 225–240. https://doi.org/10.1016/J.PATCOG.2018.03.018

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Multi-Task Structure-aware Context Modeling for Robust Keypoint-based Object Tracking

Li, X., Zhao, L., Ji, W., Wu, Y., Wu, F., Yang, M.-H., Dacheng, T., Reid, I. (2018). Multi-Task Structure-aware Context Modeling for Robust Keypoint-based Object Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2018.2818132 *In Press

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The limits and potentials of deep learning for robotics

Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M., & Corke, P. (2018). The limits and potentials of deep learning for robotics. The International Journal of Robotics Research, 37(4–5), 405–420. http://doi.org/10.1177/0278364918770733

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Automating analysis of vegetation with computer vision: Cover estimates and classification

McCool, C., Beattie, J., Milford, M., Bakker, J. D., Moore, J. L., & Firn, J. (2018). Automating analysis of vegetation with computer vision: Cover estimates and classification. Ecology and Evolution, 8(12), 6005–6015. http://doi.org/10.1002/ece3.4135

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A rapidly deployable classification system using visual data for the application of precision weed management

Hall, D., Dayoub, F., Perez, T., & McCool, C. (2018). A rapidly deployable classification system using visual data for the application of precision weed management. Computers and Electronics in Agriculture, 148, 107–120. http://doi.org/10.1016/J.COMPAG.2018.02.023

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Measures of incentives and confidence in using a social robot

Robinson, N. L., Connolly, J., Johnson, G. M., Kim, Y., Hides, L., & Kavanagh, D. J. (2018). Measures of incentives and confidence in using a social robot. Science Robotics, 3(21), eaat6963. http://doi.org/10.1126/scirobotics.aat6963

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Glare-free retinal imaging using a portable light field fundus camera

Palmer, D. W., Coppin, T., Rana, K., Dansereau, D. G., Suheimat, M., Maynard, M. Atchison, D. A., Roberts, J., Crawford, R., & Jaiprakash, A. (2018). Glare-free retinal imaging using a portable light field fundus camera. Biomedical Optics Express, 9(7), 3178. http://doi.org/10.1364/BOE.9.003178

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Learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems

James, J., Ford, J. J., & Molloy, T. L. (2018). Learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems. IEEE Robotics and Automation Letters, 3(4), 4383–4390. http://doi.org/10.1109/LRA.2018.2867237

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Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework

Jacobson, A., Chen, Z., & Milford, M. (2018). Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework. Biological Cybernetics, 1–17. http://doi.org/10.1007/s00422-017-0745-7

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Output regulation for systems on matrix Lie-groups

de Marco, S., Marconi, L., Mahony, R., & Hamel, T. (2018). Output regulation for systems on matrix Lie-groups. Automatica, 87, 8–16. https://doi.org/10.1016/J.AUTOMATICA.2017.08.006

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Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework

Jacobson, A., Chen, Z., & Milford, M. (2018). Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework. Biological Cybernetics, 1–17. http://doi.org/10.1007/s00422-017-0745-7

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Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost.

Yu, L., Jacobson, A., & Milford, M. (2018). Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost. IEEE Robotics and Automation Letters, 3(2), 811–818. http://doi.org/10.1109/LRA.2018.2792144

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Multimodal Trip Hazard Affordance Detection on Construction Sites

McMahon, S., Sunderhauf, N., Upcroft, B., & Milford, M. (2018). Multimodal Trip Hazard Affordance Detection on Construction Sites. IEEE Robotics and Automation Letters, 3(1), 1–8. http://doi.org/10.1109/LRA.2017.2719763

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Special issue on deep learning in robotics

Sünderhauf, N., Leitner, J., Upcroft, B., & Roy, N. (2018, April 27). Special issue on deep learning in robotics. The International Journal of Robotics Research. SAGE PublicationsSage UK: London, England. http://doi.org/10.1177/0278364918769189

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Multi-Modal Trip Hazard Affordance Detection On Construction Sites

McMahon, S., Sunderhauf, N., Upcroft, B., & Milford, M. (2018). Multimodal Trip Hazard Affordance Detection on Construction Sites. IEEE Robotics and Automation Letters, 3(1), 1–8. http://doi.org/10.1109/LRA.2017.2719763

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