| 2019 |
Abdi, M., Lim, C., Mohamed, S., Nahavandi, S., Abbasnejad, E., & Van Den Hengel, A. (2019). Discriminative clustering of high-dimensional data using generative modeling. In Midwest Symposium on Circuits and Systems Vol. 2018-August (pp. 799-802). Windsor, CANADA: IEEE.
DOI |
| 2018 |
Zhuang, B., Wu, Q., Shen, C., Reid, I., & Van Den Hengel, A. (2018). HCVRD: A benchmark for large-scale human-centered visual relationship detection. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 7631-7638). New Orleans: Association for the Advancement of Artificial Intelligence.
Scopus1 |
| 2018 |
Wu, Q., Wang, P., Shen, C., Reid, I., & Hengel, A. (2018). Are You Talking to Me? Reasoned Visual Dialog Generation Through Adversarial Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 6106-6115). Salt Lake City, UT: IEEE.
DOI Scopus2 |
| 2018 |
Siegersma, K., Zreik, M., Coroller, T., Dweck, M., Everett, R., Treibel, T., . . . Verjans, J. (2018). Prediction of the risk of valve surgery and adverse events in patients with aortic stenosis: myocardial tissue characterization with radiomics. In EUROPEAN HEART JOURNAL Vol. 39 (pp. 1129-1130). Munich, GERMANY: OXFORD UNIV PRESS. |
| 2018 |
Siegersma, K., Zreik, M., Coroller, T., Dweck, M., Everett, R., Treibel, T., . . . Verjans, J. (2018). Discrimination of fibrotic myocardium from healthy myocardium patients with aortic stenosis: a radiomics approach with machine learning models. In EUROPEAN HEART JOURNAL Vol. 39 (pp. 971-972). Munich, GERMANY: OXFORD UNIV PRESS. |
| 2018 |
Zhuang, B., Wu, Q., Shen, C., Reid, I., & van den Hengel, A. (2018). Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4252-4261). USA: IEEE.
DOI Scopus1 |
| 2018 |
Zhang, J., Wu, Q., Shen, C., Zhang, J., Lu, J., & van den Hengel, A. (2018). Goal-oriented visual question generation via intermediate rewards. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision - ECCV 2018: Proceedings, Part V Vol. Lecture Notes in Computer Science; vol. 11209 (pp. 189-204). Munich: Springer.
DOI |
| 2018 |
Teney, D., Anderson, P., He, X., & Van Den Hengel, A. (2018). Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge.. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. abs/1708.02711 (pp. 4223-4232). Online: IEEE.
DOI Scopus9 WoS1 |
| 2018 |
Teney, D., & Van Den Hengel, A. (2018). Visual Question Answering as a meta learning task. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision - ECCV 2018: Proceedings, Part XV Vol. 11219 LNCS (pp. 229-245). Munich: Springer.
DOI |
| 2018 |
Ma, C., Shen, C., Dick, A., Wu, Q., Wang, P., Van Den Hengel, A., & Reid, I. (2018). Visual Question Answering with memory-augmented network. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR 20418 (pp. 6975-6984). Salt Lake City, Utah: IEEE.
DOI Scopus2 |
| 2018 |
Abbasnejad, M. E., Dick, A. R., Shi, Q., & Hengel, A. V. D. (2018). Active learning from noisy tagged images. In Proceedings of BMVC 2018 and Workshops (pp. 1-13). Newcastle upon Tyne: BMVA Press. |
| 2017 |
Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., . . . Hengel, A. (2017). Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments.. In CoRR Vol. abs/1711.07280 (pp. 3674-3683). Salt Lake City, UT: IEEE.
DOI Scopus9 WoS1 |
| 2017 |
Zhu, M., Dick, A., & van den Hengel, A. (2017). Large-scale camera network topology estimation by lighting variation. In J. Blanc-Talon, R. Penne, W. Philips, D. Popescu, & P. Scheunders (Eds.), Advanced Concepts for Intelligent Vision Systems: proceedings Vol. 10617 LNCS (pp. 455-467). Antwerp, Belgium: Springer.
DOI |
| 2017 |
Gong, D., Yang, J., Liu, L., Zhang, Y., Reid, I., Shen, C., . . . Shi, Q. (2017). From motion blur to motion flow: a deep learning solution for removing heterogeneous motion blur. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 3806-3815). Piscataway, NJ: IEEE.
DOI Scopus16 WoS1 |
| 2017 |
Abbasnejad, M., Dick, A., & van den Hengel, A. (2017). Infinite variational autoencoder for semi-supervised learning. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 781-790). Honolulu: IEEE.
DOI Scopus6 WoS1 |
| 2017 |
Wang, P., Wu, Q., Shen, C., & van den Hengel, A. (2017). The VQA-machine: learning how to use existing vision algorithms to answer new questions. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 3909-3918). Honolulu: IEEE.
DOI Scopus12 WoS2 |
| 2017 |
Wang, P., Liu, L., Shen, C., Huang, Z., van den Hengel, A., & Shen, H. (2017). Multi-attention network for one shot learning. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 6212-6220). http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8097368: IEEE.
DOI Scopus11 WoS4 |
| 2017 |
Li, Y., Lin, G., Zhuang, B., Liu, L., Shen, C., & van den Hengel, A. (2017). Sequential person recognition in photo albums with a recurrent network. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 5660-5668). Honolulu: IEEE.
DOI Scopus7 |
| 2017 |
Gong, D., Tan, M., Zhang, Y., Hengel, A., & Shi, Q. (2017). Self-paced kernel estimation for robust blind image deblurring. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017) Vol. 2017 (pp. 1670-1679). Piscataway, NJ: IEEE.
DOI Scopus6 WoS2 |
| 2017 |
Lu, H., Zhang, L., Cao, Z., Wei, W., Xian, K., Shen, C., & Hengel, A. (2017). When Unsupervised Domain Adaptation Meets Tensor Representations. In Proceedings: 2017 IEEE International Conference on Computer Vision Vol. 2017-October (pp. 599-608). Venice, Italy: IEEE.
DOI Scopus6 WoS1 |
| 2017 |
Johnston, A., Garg, R., Carneiro, G., Reid, I., & van den Hengel, A. (2017). Scaling CNNs for high resolution volumetric reconstruction from a single image. In Proceedings of the IEEE International Conference on Computer Vision Workshop (ICCVW 2017) Vol. 2018-January (pp. 930-939). Piscataway, NJ: IEEE.
DOI Scopus2 |
| 2017 |
Teney, D., Liu, L., & van den Hengel, A. (2017). Graph-structured representations for visual question answering. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 3233-3241). Piscataway, NJ: IEEE.
DOI Scopus16 WoS1 |
| 2017 |
Zhang, Z., Shi, Q., McAuley, J., Wei, W., Zhang, Y., Yao, R., & Van Den Hengel, A. (2017). Solving constrained combinatorial optimization problems via MAP inference without high-order penalties. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 3804-3810). San Francisco: AAAI. |
| 2017 |
Gong, D., Tan, M., Zhang, Y., Van Den Hengel, A., & Shi, Q. (2017). MPGL: An efficient matching pursuit method for generalized LASSO. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 1934-1940). San Francisco: AAAI.
Scopus5 |
| 2017 |
Wang, P., Wu, Q., Shen, C., Dick, A., & Van Den Hengel, A. (2017). Explicit knowledge-based reasoning for visual question answering. In C. Sierra (Ed.), Proceedings of the twenty-sixth International Joint Conference on Artificial Intelligence (pp. 1290-1296). Melbourne: International Joint Conferences on Artificial Intelligence.
DOI Scopus8 |
| 2016 |
Tan, M., Yan, Y., Wang, L., Van Den Hengel, A., Tsang, I., & Shi, Q. (2016). Learning sparse confidence-weighted classifier on very high dimensional data. In Proceedings of the 30th AAAI Conference on Artificial Intelligence Vol. 3 (pp. 2080-2086). Phoenix, AZ: AAAI Press.
Scopus1 |
| 2016 |
Teney, D., Liu, L., & Hengel, A. V. D. (2016). Graph-Structured Representations for Visual Question Answering.. In CoRR Vol. abs/1609.05600. |
| 2016 |
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 Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 2249-2257). Las Vegas, NV: IEEE.
DOI Scopus43 WoS22 |
| 2016 |
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 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 4622-4630). Las Vegas, NV: IEEE.
DOI Scopus73 WoS25 |
| 2016 |
Lin, G., Shen, C., Van Den Hengel, A., & Reid, I. (2016). Efficient piecewise training of deep structured models for semantic segmentation. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Vol. 2016-December (pp. 3194-3203). Las Vegas, NV: IEEE.
DOI Scopus200 WoS7 |
| 2016 |
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 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 203-212). Las Vegas, NV: IEEE.
DOI Scopus136 WoS54 |
| 2016 |
Tan, M., Xiao, S., Gao, J., Xu, D., Van Den Hengel, A., & Shi, Q. (2016). Proximal riemannian pursuit for large-scale trace-norm minimization. In Proceedings of the I29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Vol. 2016-December (pp. 5877-5886). Las Vegas, NV: IEEE.
DOI Scopus1 |
| 2016 |
Wang, P., Liu, L., Shen, C., Huang, Z., Van Den Hengel, A., & Shen, H. (2016). What's wrong with that object? Identifying images of unusual objects by modelling the detection score distribution. In Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 1573-1581). Las Vegas, NV: IEEE.
DOI Scopus5 WoS3 |
| 2016 |
Zhang, Z., Shi, Q., McAuley, J., Wei, W., Zhang, Y., & Van Den Hengel, A. (2016). Pairwise matching through max-weight bipartite belief propagation. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2016) Vol. 2016 (pp. 1202-1210). Las Vegas, NV: IEEE.
DOI Scopus16 WoS6 |
| 2016 |
Gong, D., Tan, M., Zhang, Y., Van Den Hengel, A., & Shi, Q. (2016). Blind image deconvolution by automatic gradient activation. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Vol. 2016-December (pp. 1827-1836). Las Vegas, NV: IEEE.
DOI Scopus24 WoS12 |
| 2016 |
Zhang, L., Wei, W., Zhang, Y., Shen, C., Van Den Hengel, A., & Shi, Q. (2016). Cluster sparsity field for hyperspectral imagery denoising. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision Vol. 9909 (pp. 631-647). Amsterdam, Netherlands: Springer International Publishing AG.
DOI Scopus9 WoS8 |
| 2016 |
Li, Y., Liu, L., Shen, C., & van den Hengel, A. (2016). Image co-localization by mimicking a good detector’s confidence score distribution. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision, Part II Vol. 9906 LNCS (pp. 19-34). Amsterdam, Netherlands: Springer International Publishing.
DOI Scopus10 WoS7 |
| 2015 |
Lin, G., Shen, C., Reid, I., & Van Den Hengel, A. (2015). Deeply learning the messages in message passing inference. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: 29th Annual Conference on Neural Information Processing Systems 2015 Vol. 2015-January (pp. 361-369). Montreal: Neural Information Processing Systems.
Scopus17 |
| 2015 |
Li, Y., Liu, L., Shen, C., & Van Den Hengel, A. (2015). Mid-level deep pattern mining. In Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 971-980). Boston, MA: IEEE.
DOI Scopus50 WoS27 |
| 2015 |
Szpak, Z., Chojnacki, W., & Van Den Hengel, A. (2015). Robust multiple homography estimation: an ill-solved problem. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 2132-2141). Boston, MA: IEEE.
DOI Scopus6 WoS3 |
| 2015 |
Wang, P., Shen, C., & Van Den Hengel, A. (2015). Efficient SDP inference for fully-connected CRFs based on low-rank decomposition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 3222-3231). Boston, MA: IEEE.
DOI Scopus6 |
| 2015 |
Tan, M., Shi, Q., Van Den Hengel, A., Shen, C., Gao, J., Hu, F., & Zhang, Z. (2015). Learning graph structure for multi-label image classification via clique generation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 4100-4109). Boston, MA: IEEE.
DOI Scopus13 WoS6 |
| 2015 |
Van Den Hengel, A., Russell, C., Dick, A., Bastian, J., Pooley, D., Fleming, L., & Agapito, L. (2015). Part-based modelling of compound scenes from images. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 878-886). Boston, MA: IEEE.
DOI Scopus5 WoS3 |
| 2015 |
Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2015). Learning to rank in person re-identification with metric ensembles. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 1846-1855). Boston, MA: IEEE.
DOI Scopus216 WoS122 |
| 2015 |
Liu, L., Shen, C., & van den Hengel, A. (2015). The treasure beneath convolutional layers: cross-convolutional-layer pooling for image classification. In Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 4749-4757). Boston, MA: IEEE.
DOI Scopus82 WoS26 |
| 2015 |
Li, B., Shen, C., Dai, Y., Van Den Hengel, A., & He, M. (2015). Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 1119-1127). Boston, MA: IEEE.
DOI Scopus131 WoS67 |
| 2015 |
Paisitkriangkrai, S., Sherrah, J., Janney, P., & Van Den Hengel, A. (2015). Effective semantic pixel labelling with convolutional networks and Conditional Random Fields. In Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2015-October (pp. 36-43). Boston, MA: IEEE.
DOI Scopus87 WoS13 |
| 2015 |
McAuley, J., Targett, C., Shi, Q., & Van Den Hengel, A. (2015). Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 43-52). Santiago, Chile: Association for Computing Machinery.
DOI Scopus285 WoS129 |
| 2015 |
Faulkner, H., Shehu, E., Szpak, Z., Chojnacki, W., Tapamo, J., Dick, A., & Van Den Hengel, A. (2015). A study of the region covariance descriptor: impact of feature selection and image transformations. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). Adelaide, SA.: IEEE.
DOI Scopus3 |
| 2015 |
Zhu, M., Dick, A., & Van Den Hengel, A. (2015). Camera network topology estimation by lighting variation. In Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications (pp. 238-243). Adelaide, Australia: IEEE.
DOI Scopus2 |
| 2015 |
Ward, B., Bastian, J., van den Hengel, A., Pooley, D., Rajendra, B., Berger, B., & Tester, M. (2015). A model-based approach to recovering the structure of a plant from images. In L. Agapito, M. Bronstein, & C. Rother (Eds.), Proceedings of the 13th European Conference on Computer Vision Workshops (ECCV 2014), as published in Lecture Notes in Computer Science Vol. 8928 (pp. 215-230). Switzerland: Springer International Publishing.
DOI Scopus1 WoS1 |
| 2014 |
Li, B., Dai, Y., He, M., & Van Den Hengel, A. (2014). A relaxation method to articulated trajectory reconstruction from monocular image sequence. In Proceedings: 2014 IEEE China Summit & International Conference on Signal and Information Processing (pp. 389-393). Xi'an, China: IEEE.
DOI Scopus1 |
| 2014 |
Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). Strengthening the effectiveness of pedestrian detection with spatially pooled features. In Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014: Proceedings, Part IV Vol. 8692 LNCS (pp. 546-561). Zurich, Switzerland: Springer International Publishing.
DOI Scopus86 WoS58 |
| 2014 |
Lin, G., Shen, C., Shi, Q., Van Den Hengel, A., & Suter, D. (2014). Fast supervised hashing with decision trees for high-dimensional data. In Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1971-1978). Columbus, Ohio: IEEE.
DOI Scopus200 WoS76 |
| 2014 |
Liu, L., Shen, C., Wang, L., Van Den Hengel, A., & Wang, C. (2014). Encoding high dimensional local features by sparse coding based fisher vectors. In Proceedings of the 27th International Conference on Neural Information Processing Systems Vol. 2 (pp. 1143-1151). Online: MIT Press.
Scopus47 |
| 2013 |
Lin, G., Shen, C., Suter, D., & Van Den Hengel, A. (2013). A general two-step approach to learning-based hashing. In Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 2013 (pp. 1-13). USA: IEEE Computer Society.
DOI Scopus103 WoS58 |
| 2013 |
Li, Y., Shen, C., Jia, W., & Van Den Hengel, A. (2013). Leveraging surrounding context for scene text detection. In Proceedings of the IEEE 2013 International Conference on Image Processing, ICIP 2013 (pp. 2264-2268). USA: IEEE.
DOI Scopus14 WoS11 |
| 2013 |
Li, X., Li, Y., Shen, C., Dick, A., & Van Den Hengel, A. (2013). Contextual hypergraph modeling for salient object detection. In Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 2013 (pp. 3328-3335). USA: IEEE Computer Society.
DOI Scopus119 WoS81 |
| 2013 |
Lin, G., Shen, C., & Van Den Hengel, A. (2013). Approximate constraint generation for efficient structured boosting. In Proceedings of the 2013 IEEE International Conference on Image Processing (pp. 4287-4291). USA: IEEE.
DOI |
| 2013 |
Zhang, C., Bastian, J., Shen, C., Van Den Hengel, A., & Shen, T. (2013). Extended depth-of-field via focus stacking and graph cuts. In Proceedings of the 2013 IEEE Conference on Image Processing, ICIP 2013 (pp. 1272-1276). USA: IEEE.
DOI Scopus8 WoS6 |
| 2013 |
Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2013). Efficient pedestrian detection by directly optimizing the partial area under the ROC curve. In Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013 (pp. 1057-1064). USA: IEEE.
DOI Scopus33 WoS14 |
| 2013 |
Li, X., Lin, G., Shen, C., Van Den Hengel, A., & Dick, A. (2013). Learning hash functions using column generation. In Proceedings of the 30th International Conference on Machine Learning, IMLS 2013 (pp. 1-9). online: IMCL.
Scopus80 |
| 2013 |
Yao, R., Shi, Q., Shen, C., Zhang, Y., & Van Den Hengel, A. (2013). Part-based visual tracking with online latent structural learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2363-2370). United States of America: IEEE.
DOI Scopus166 WoS114 |
| 2013 |
Wang, Z., Shi, Q., Shen, C., & Van Den Hengel, A. (2013). Bilinear programming for human activity recognition with unknown MRF graphs. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1690-1697). United States of America: IEEE.
DOI Scopus15 WoS5 |
| 2013 |
Shen, F., Shen, C., Shi, Q., Van Den Hengel, A., & Tang, Z. (2013). Inductive hashing on manifolds. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1562-1569). United States of America: IEEE.
DOI Scopus138 WoS96 |
| 2013 |
Wang, P., Shen, C., & Van Den Hengel, A. (2013). A fast semidefinite approach to solving binary quadratic problems. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1312-1319). United States: IEEE.
DOI Scopus15 WoS6 |
| 2013 |
Li, X., Shen, C., Dick, A., & Van Den Hengel, A. (2013). Learning compact binary codes for visual tracking. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2419-2426). United States: IEEE.
DOI Scopus68 WoS48 |
| 2012 |
Shi, Q., Shen, C., Hill, R., & Van Den Hengel, A. (2012). Is margin preserved after random projection?. In Proceedings of the29th International Conference on Machine Learning, ICML 12 Vol. 1 (pp. 591-598). USA: Omnipress.
Scopus27 |
| 2012 |
Szpak, Z., Chojnacki, W., & Van Den Hengel, A. (2012). A comparison of ellipse fitting methods and implications for multiple-view geometry estimation. In Proceedings of Digital Image Computing Techniques and Applications, DICTA 2012 (pp. 1-8). USA: IEEE.
DOI Scopus5 |
| 2012 |
Lin, G., Shen, C., Van Den Hengel, A., & Suter, D. (2012). Fast training of effective multi-class boosting using coordinate descent optimization. In Proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012 Vol. 7725 LNCS (pp. 782-793). Germany: Springer-Verlag.
DOI |
| 2012 |
Szpak, Z., Chojnacki, W., & Van Den Hengel, A. (2012). Guaranteed ellipse fitting with the Sampson distance. In Proceedings of the12th European Conference on Computer Vision, ECCV 2012 Vol. 7576 LNCS (pp. 87-100). Germany: Springer-Verlag.
DOI Scopus26 WoS14 |
| 2012 |
Yao, R., Shi, Q., Shen, C., Zhang, Y., & Van Den Hengel, A. (2012). Robust tracking with weighted online structured learning. In Proceedings of the 2012 European Conference on Computer Vision, ECCV 2012 Vol. 7574 LNCS (pp. 158-172). Germany: Springer-Verlag.
DOI Scopus21 WoS13 |
| 2012 |
Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2012). Sharing features in multi-class boosting via group sparsity. In Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (pp. 2128-2135). USA: IEEE.
DOI Scopus6 WoS3 |
| 2012 |
Li, X., Shen, C., Shi, Q., Dick, A., & Van Den Hengel, A. (2012). Non-sparse linear representations for visual tracking with online reservoir metric learning. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (pp. 1760-1767). USA: IEEE.
DOI Scopus64 WoS35 |
| 2011 |
Li, X., Dick, A., Wang, H., Shen, C., & Van Den Hengel, A. (2011). Graph mode-based contextual kernels for robust SVM tracking. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2011 (pp. 1156-1163). USA: IEEE.
DOI Scopus30 WoS17 |
| 2011 |
Shi, Q., Eriksson, A., Van Den Hengel, A., & Shen, C. (2011). Is face recognition really a compressive sensing problem?. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 553-560). USA: IEEE.
DOI Scopus236 WoS156 |
| 2011 |
Chojnacki, W., & Van Den Hengel, A. (2011). A dimensionality result for multiple homography matrices. In 2011 IEEE International Conference on Computer Vision (pp. 2104-2109). 345 E 47TH ST, NEW YORK, NY 10017 USA: IEEE.
DOI Scopus4 WoS2 |
| 2010 |
Chojnacki, W., Szpak, Z., Brooks, M., & Van Den Hengel, A. (2010). Multiple homography estimation with full consistency constraints. In Proceedings of DICTA 2010 (pp. 480-485). USA: IEEE.
DOI Scopus7 |
| 2010 |
Chen, Y., Dick, A., & Van Den Hengel, A. (2010). Image retrieval with a visual thesaurus. In Proceedings of 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2010) (pp. 8-14). USA: IEEE.
DOI Scopus2 |
| 2010 |
Van Den Hengel, A. (2010). Image-based modelling for augmenting reality. In Proceedings of 2010 International Symposium on Ubiquitous Virtual Reality, ISUVR 2010 (pp. 1-4). USA: IEEE.
DOI Scopus2 |
| 2010 |
Bastian, J., Ward, B., Hill, R., Van Den Hengel, A., & Dick, A. (2010). Interactive modelling for AR applications. In Proceedings of IEEE International Symposium on Mixed and Augmented Reality 2010 Science and Technology (pp. 199-205). USA: IEEE.
DOI Scopus19 |
| 2010 |
Eriksson, A., & Van Den Hengel, A. (2010). Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L₁ norm. In Proceedings of 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (pp. 771-778). 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA: IEEE COMPUTER SOC.
DOI Scopus130 WoS83 |
| 2009 |
Eriksson, A., & Van Den Hengel, A. (2009). Optimization on the manifold of multiple homographies. In Proceedings of the 2009 IEEE ICCV Workshops (pp. 242-249). USA: IEEE.
DOI Scopus5 |
| 2009 |
Van Den Hengel, A., Hill, R., Ward, B., Cichowski, A., Detmold, H., Madden, C., . . . Bastian, J. (2009). Automatic camera placement for large scale surveillance networks. In Proceedings of WACV 2009 (pp. 1-6). USA: IEEE.
DOI Scopus17 |
| 2009 |
Van Den Hengel, A., Hill, R., Ward, B., & Dick, A. (2009). In situ image-based modeling. In Proceedings of the 8th International Symposium on Mixed & Augmented Reality 2009 (pp. 107-110). USA: IEEE.
DOI Scopus32 WoS11 |
| 2009 |
Shen, C., Kim, J., Wang, L., & Van Den Hengel, A. (2009). Positive Semidefinite Metric Learning with Boosting. In Proceedings of NIPS 2009 (pp. 1651-1660). online: NIPS.
Scopus52 |
| 2009 |
Hill, R., Madden, C., Van Den Hengel, A., Detmold, H., & Dick, A. (2009). Measuring latency for video surveillance systems. In Proceedings of 2009 Digital Image Computing: Techniques and Applications (pp. 89-95). USA: IEEE.
DOI Scopus17 WoS7 |
| 2009 |
Chojnacki, W., Hill, R., Van Den Hengel, A., & Brooks, M. (2009). Multi-projective Parameter Estimation for Sets of Homogeneous Matrices. In Proceedings of 2009 Digital Image Computing: Techniques and Applications (pp. 119-124). California: IEEE.
DOI Scopus2 WoS1 |
| 2009 |
Cichowski, A., Madden, C., Detmold, H., Dick, A., Van Den Hengel, A., & Hill, R. (2009). Tracking hand-off in large surveillance networks. In Proceeding of the 24th International Conference Image and Vision Computing New Zealand (VCNZ 2009) (pp. 276-281). USA: IEEE.
DOI Scopus9 WoS6 |
| 2009 |
Detmold, H., Van Den Hengel, A., Dick, A., Madden, C., Cichowski, A., & Hill, R. (2009). Surprisal-aware scheduling of PTZ cameras. In Proceedings of ICDSC 2009 (pp. 1-8). USA: IEEE.
DOI Scopus1 |
| 2009 |
Van Den Hengel, A., Detmold, H., Madden, C., Dick, A., Cichowski, A., & Hill, R. (2009). A framework for determining overlap in large scale networks. In Proceedings of ICDSC 2009 (pp. 1-8). USA: IEEE.
DOI Scopus2 |
| 2009 |
Cichowski, A., Madden, C., Van Den Hengel, A., Hill, R., Detmold, H., & Dick, A. (2009). Contradiction and correlation for camera overlap estimation. In Carlo Regazzoni (Ed.), Proceedings of the 2009 6th IEEE International Conference on Advanced Video & Signal Based Surveillance (pp. 1-6). USA: IEEE.
DOI Scopus1 |
| 2008 |
Van Den Hengel, A., Hill, R., Detmold, H., & Dick, A. (2008). Searching in space and time: a system for forensic analysis of large video repositories. In Proceedings of the 1st international conference on forensic application and techniques in telecommunications, information and multimedia workshop (pp. 1-6). Australia: IEEE.
Scopus2 |
| 2008 |
Detmold, H., Van Den Hengel, A., Dick, A., Cichowski, A., Hill, R., Kocadag, E., . . . Munro, D. (2008). Estimating camera overlap in large and growing networks. In Proceedings of the ICDSC 2008 (pp. 1-9). USA: IEEE.
DOI Scopus10 |
| 2008 |
Hill, R., Van Den Hengel, A., Dick, A., Cichowski, A., & Detmold, H. (2008). Empirical evaluation of the exclusion approach to estimating camera overlap. In Proceedings of the ICDSC 2008 (pp. 1-9). USA: IEEE.
DOI Scopus8 |
| 2007 |
Van Den Hengel, A., Detmold, H., Dick, A., & Hill, R. (2007). Preparing for post-catastrophe video processing. In Priyan Mendis (Ed.), Proceedings of the 2007 RNSA Security Technology Conference (pp. 1-8). Australia: Australian Homeland Security Research Centre. |
| 2007 |
Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2007). A shape hierarchy for 3D modelling from video. In Andrew Rohl (Ed.), Proceedings of GRAPHITE 2007 (pp. 63-70). Australia: ACM.
DOI Scopus2 WoS3 |
| 2007 |
Pooley, D., Brooks, M., & Van Den Hengel, A. (2007). RATSAC: An adaptive method for accelerated robust estimation and its application to video synchronisation. In M. Bottema (Ed.), Proceedings of DICTA 2007 (pp. 294-300). CDROM: IEEE.
DOI Scopus1 |
| 2007 |
Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2007). Interactive 3D model completion. In M. Bottema (Ed.), Proceedings of DICTA 2007 (pp. 175-181). CDROM: IEEE.
DOI Scopus3 |
| 2007 |
Flint, A., Dick, A., & Van Den Hengel, A. (2007). Thrift: Local 3D structure recognition. In M. Bottema (Ed.), Proceedings of DICTA 2007 (pp. 182-188). CDROM: IEEE.
DOI Scopus80 |
| 2007 |
Detmold, H., Van Den Hengel, A., Dick, A., Cichowski, A., Hill, R., Kocadag, E., . . . Munro, D. (2007). Topology estimation for thousand-camera surveillance networks. In B. Rinner, & W. Wolf (Eds.), Proceedings of ICDSC-07 (pp. 195-202). CDROM: IEEE.
DOI Scopus27 |
| 2007 |
Van Den Hengel, A., Dick, A., Detmold, H., Cichowski, A., & Hill, R. (2007). Finding camera overlap in large surveillance networks. In Yasushi Yagi (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4843 LNCS (PART 1) Vol. 4843 LNCS (pp. 375-384). Germany: Springer.
DOI Scopus7 WoS7 |
| 2007 |
Kumar, P., Brooks, M., & Van Den Hengel, A. (2007). An adaptive Bayesian technique for tracking multiple objects. In Ashish Ghosh (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4815 LNCS Vol. 4815 LNCS (pp. 657-665). Germany: Springer.
DOI Scopus1 WoS1 |
| 2007 |
Van Den Hengel, A., Chojnacki, W., & Brooks, M. (2007). Determining the translational speed of a camera from time-varying optical flow. In B. Jahne (Ed.), Complex motion [electronic resource]: first international workshop, IWCM 2004, Gunzburg, Germany, October 12-14, 2004: revised papers Vol. 3417 LNCS (pp. 1-8). Germany: Springer.
DOI Scopus2 WoS2 |
| 2007 |
Chojnacki, W., van den Hengel, A., & Brooks, M. (2007). Generalised Principal Component Analysis: exploiting inherent parameter constraints. In J. Braz, A. Ranchordas, H. Araujo, & J. Jorge (Eds.), Advances in computer graphics and computer vision: International Conferences VISAPP and GRAPP 2006 Vol. 4 CCIS (pp. 217-228). Setubal, Portugal: Springer.
DOI Scopus2 |
| 2006 |
Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2006). Building models of regular scenes from structure-and-motion. In M. J. Chandler, E. Trucco, & R. B. Fisher (Eds.), Proceedings of British Machine Vision Conference 2006 (pp. CDROM1-CDROM9). CDROM: BMVA.
Scopus8 |
| 2006 |
Chojnacki, W., Van Den Hengel, A., & Brooks, M. (2006). Constrained generalised principal component analysis. In A. Ranchordas, H. Araujo, & B. Encarnacao (Eds.), Proceedings of VISAPP 2006 Vol. 1 (pp. CDROM206-CDROM212). CDROM: INSTICC. |
| 2006 |
Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2006). Rapid interactive modelling from video with graph cuts. In D. Fellner, & C. Hansen (Eds.), Proceedings of Eurographics 2006 (pp. CDROM1-CDROM4). CDROM: Eurographics Association. |
| 2006 |
Detmold, H., Dick, A., Falkner, K., Munro, D., Van Den Hengel, A., & Morrison, R. (2006). Middleware for video surveillance networks. In V. Cahill, & S. Michiels (Eds.), Proceedings of MIdSens'06 Vol. 218 (pp. CDROM31-CDROM36). CDROM: ACM Press.
DOI Scopus17 |
| 2006 |
Van Den Hengel, A., Dick, A., Thormaehlen, T., Torr, P., & Ward, B. (2006). Fitting multiple models to multiple images with minimal user interaction. In R. Horaud, C. Schnorr, P. Torr, & J. Tsotsos (Eds.), Proceedings of WRUPKV 2006 (pp. CDROM1-CDROM15). CDROM: University of Ljubljana. |
| 2006 |
Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2006). Hierarchical model fitting to 2D and 3D data. In E. Banissi, M. Sarfraz, M. Huang, & Q. Wu (Eds.), Proceedings of Computer Graphics, Imaging and Visualisation Vol. 2006 (pp. 359-364). USA: IEEE.
DOI Scopus1 |
| 2006 |
Detmold, H., Dick, A., Falkner, K., Munro, D., Van Den Hengel, A., & Morrison, R. (2006). Scalable surveillance software architecture. In M. Piccardi, T. Hintz, I. Pavlidis, C. Regazzoni, & X. He (Eds.), Proceedings of AVSS 2006 (pp. CDROM1-CDROM6). CDROM: IEEE.
DOI Scopus10 |
| 2006 |
Van Den Hengel, A., Dick, A., & Hill, R. (2006). Activity topology estimation for large networks of cameras. In M. Piccardi, T. Hintz, I. Pavlidis, C. Regazzoni, & X. He (Eds.), Proceedings of AVSS 2006 (pp. CDROM1-CDROM6). CDROM: IEEE.
DOI Scopus21 |
| 2005 |
Bastian, J., & Van Den Hengel, A. (2005). Computing surface-based photo-consistency on graphics hardware. In B. Lovell, A. Maeder, T. Caelli, & S. Ourselin (Eds.), Proceedings of the 8th Biennial Conference of the Australian Pattern Recognition Society: 'Digital Image Computing: Techniques and Applications' Vol. 2005 (pp. CD ROM 1-CD ROM 8). CD ROM: IEEE Computer Society.
DOI Scopus1 |
| 2005 |
Brooks, M., Dick, A., & Van Den Hengel, A. (2005). Towards intelligent networked video surveillance for the detection of suspicious behaviours. In P. Mendis, J. Lai, & E. Dawson (Eds.), Proceedings of the 2005 Science, Engineering and Technology Summit (pp. 153-161). Curtin, ACT: The Australian Homeland Security Research Centre. |
| 2005 |
Shen, C., Van Den Hengel, A., & Brooks, M. (2005). Visual tracking via efficient kernel discriminant subspace learning. In C. Regazzoni, & F. De Natale (Eds.), Proceedings of the IEEE International Conference on Image Processing Vol. 2 (pp. 1-4). USA: IEEE.
DOI Scopus7 |
| 2005 |
Shen, C., Brooks, M., & Van Den Hengel, A. (2005). Augmented particle filtering for efficient visual tracking. In C. Regazzoni, & F. De Natale (Eds.), Proceedings of the IEEE International Conference on Image Processing Vol. 3 (pp. 1-4). USA: IEEE.
DOI Scopus6 |
| 2005 |
Shen, C., Brooks, M., & Van Den Hengel, A. (2005). Fast global kernel density mode seeking with application to localisation and tracking. In S. Ma, & H. Shum (Eds.), Proceedings of the Tenth IEEE International Conference on Computer Vision Vol. II (pp. 1516-1523). Los Alamitos, California: IEEE.
DOI Scopus35 WoS8 |
| 2005 |
Hill, R., & Van Den Hengel, A. (2005). Experiences with simulated robot soccer as a teaching tool. In Third International Conference on Information Technology and Applications: 4-7 July 2005, Sydney, Australia: proceedings Vol. I (pp. 387-390). Online: IEEE Computer Society.
DOI Scopus5 WoS1 |
| 2004 |
Shen, C., Van Den Hengel, A., Dick, A., & Brooks, M. (2004). Enhanced importance sampling: Unscented auxiliary particle filtering for visual tracking. In G. Webb, & X. Yu (Eds.), Proceedings of the 17th Australasian Joint Conference on Artificial Intelligence 2004 Vol. 3339 (pp. 180-191). Berlin, Germany: Springer.
DOI Scopus9 WoS5 |
| 2004 |
Shen, C., Van Den Hengel, A., Dick, A., & Brooks, M. (2004). 2D articulated tracking with dynamic Bayesian networks. In D. Wei, H. Wang, Z. Peng, A. Kara, & Y. He (Eds.), Proceedings of the 4th International Conference on Computer and Information Technology 2004 (pp. 1-7). Los Alamitos, California, USA: IEEE.
DOI Scopus8 WoS1 |
| 2003 |
Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2003). FNS, CFNS, and HEIV: extending three vision parameter estimation methods. In C. Sun, H. Talbot, S. Ourselin, & T. Adriaansen (Eds.), Digital Image Computing: Techniques and Applications - Proceedings of the VIIth Biennial Australian Pattern Recognition Society Conference - DICTA 2003 (pp. 449-458). Victoria, Australia: CSIRO Publishing. |
| 2003 |
Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2003). FNS and HEIV: relating two vision parameter estimation frameworks. In M. Feretti (Ed.), Proceedings of the 12th International Conference on Image Analysis and Processing 2003 (pp. 152-157). California, USA: IEEE.
DOI Scopus2 WoS1 |
| 2003 |
Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2003). A statistical rationalisation of Hartley's normalised eight-point algorithm. In M. Feretti (Ed.), Proceedings of the 12th International Conference on Image Analysis and Processing 2003 (pp. 334-339). California, USA: IEEE.
DOI Scopus4 |
| 2003 |
Van Den Hengel, A., Hill, R., & Brooks, M. (2003). Incorporating constraints into the design of locally identifiable calibration patterns. In L. Torres (Ed.), Proceedings of the IEEE International Conference on Image Processing 2003 Vol. 1 (pp. CDROM 1-CDROM 4). CDROM: IEEE.
DOI Scopus1 |
| 2003 |
Pooley, D., Brooks, M., Van Den Hengel, A., & Chojnacki, W. (2003). A voting scheme for estimating the synchrony of moving-camera videos. In L. Torres (Ed.), Proceedings of the 2003 IEEE International Conference on Image Processing Vol. 1 (pp. CDROM 1-CDROM 4). CDROM: IEEE.
DOI Scopus20 WoS2 |
| 2003 |
Shen, C., Van Den Hengel, A., & Dick, A. (2003). Probabilistic multiple cue intergration for particle filter based tracking. In C. Sun, H. Talbot, S. Ourselin, & T. Adriaansen (Eds.), Proceedings of the 7th Biennial Australian Pattern Recognition Society Conference - DICTA 2003 (pp. 399-408). Australia: CSIRO. |
| 2003 |
Bastian, J., & Van Den Hengel, A. (2003). Computing image-based reprojection error on graphics hardware. In C. Sun, H. Talbot, S. Ourselin, & T. Adriaansen (Eds.), Proceedings of the 7th Biennial Australian Pattern Recognition Society Conference - DICTA 2003 (pp. 663-676). Australia: CSIRO. |
| 2002 |
Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2002). A new approach to constrained parameter estimation applicable to some computer vision problems. In D. Suter (Ed.), Proceedings of the Statistical Methods in Video Processing Workshop (pp. 43-48). Victoria, Australia: Monash University. |
| 2002 |
Van Den Hengel, A., Chojnacki, W., Brooks, M., & Gawley, D. (2002). A new constrained parameter estimator: experiments in fundamental matrix computation. In P. L. Rosin, & D. Marshall (Eds.), Proceedings of the 13th British Machine Vision Conference (pp. 468-476). UK: BMVA. |
| 2002 |
Hengel, A. V. D., Brooks, M. J., Chojnacki, W., & Gawley, D. (2002). A new constrained parameter estimator: experiments in fundamental matrix computation. In P. L. Rosin, & A. D. Marshall (Eds.), Proceedings of the British Machine Vision Conference 2002, BMVC 2002 (pp. 468-476). Cardiff, UK: British Machine Vision Association.
DOI |
| 2001 |
Brooks, M., Chojnacki, W., Gawley, D., & Van Den Hengel, A. (2001). Is covariance information useful in estimating vision parameters?. In S. F. El-Hakim, & A. Gruen (Eds.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 4309 (pp. 195-203). PO BOX 10 BELLINGHAM WASHINGTON USA: THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS.
DOI |
| 2001 |
Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2001). A fast MLE-based method for estimating the fundamental matrix. In P. I. Pitas (Ed.), Proceedings of the International Conference on Image Processing Vol. 2 (pp. CDROM 1-CDROM 4). CD-ROM: THE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS.
DOI Scopus2 |
| 2001 |
Brooks, M., Chojnacki, W., Gawley, D., & Van Den Hengel, A. (2001). What value covariance information in estimating vision parameters?. In Bob Werner (Ed.), Proceedings of the Eighth IEEE International Conference on Computer Vision Vol. 1 (pp. 302-308). LOS ALAMITOS, CALIFORNIA, USA: IEEE COMPUTER SOCIETY.
DOI Scopus36 WoS7 |
| 2000 |
Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2000). Estimating vision parameters given data with covariances. In M. Mirmehdi, & B. Thomas (Eds.), Proceedings of the 11th British Machine Vision Conference 2000 (pp. 182-191). Bristol, UK: ILES Central Press. |
| 2000 |
Chojnacki, W., Brooks, M., & Van Den Hengel, A. (2000). A simplified treatment of Kanatani's renormalisation method. In J. Wang (Ed.), Proceedings of ICARV 2000 - Sixth International Conference on Control, Automation, Robotics and Vision (pp. CD). Singapore: School of Electrical & Electronic Engineering, NTU. |
| 2000 |
Chojnacki, W., Brooks, M., & Van Den Hengel, A. (2000). A framework for understanding renormalisation-type methods in computer vision. In J. Blanc Talon, D. Popescu, & G. E. Lasker (Eds.), Proceedings of Advanced Concepts for Intelligent Vision Systems ACIVS - 2000 (pp. 13-19). Ontario, Canada: The International Institute for Advanced Studies in Systems Research and Cybernetics. |
| 2000 |
Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2000). A fast MLE-based method for estimating the fundamental matrix. In C. Sun, P. Ogunbona, W. Li, & R. Beare (Eds.), Proceedings of APRS/IEEE Workshop on Stereo Image and Video Processing (pp. 33-36). Australia: Australian Pattern Recognition Society. |
| 1998 |
Brooks, M., Chojnacki, W., Dick, A., van den Hengel, A., Kanatani, K., & Ohta, N. (1998). Incorporating optical flow uncertainty information into a self-calibration procedure for a moving camera. In S. ElHakim, & A. Gruen (Eds.), Proceedings of SPIE Vol. 3641 Videometrics VI (pp. 183-192). San Jose, CA: SPIE.
DOI Scopus1 |
| 1998 |
Brooks, M., Chojnacki, W., van den Hengel, A., & Baumela, L. (1998). Robust techniques for the estimation of structure from motion in the uncalibrated case. In H. Burkhardt, & B. Neumann (Eds.), Computer Vision - ECCV'98: proceedings vol.1 Vol. 1406 (pp. 281-295). Freiburg, Germany: Springer.
DOI Scopus3 |
| 1995 |
Brooks, M., Chojnacki, W., & van den Hengel, A. (1995). Solving the shape-from-shading problem on the CM-5. In Proceedings: CAMP '95 Conference on Computer Architectures for Machine Perception (pp. 196-201). Como, Italy: IEEE.
DOI |
