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Professor David Suter

Professor of Computer Science

Staff Member Details
Telephone: +61 8 6304 6591
Email: d.suter@ecu.edu.au
Campus: Joondalup  
Room: JO18.417  
ORCID iD: https://orcid.org/0000-0001-6306-3023

David Suter is a Professor of Computer Science in the School of Science (Computing and Security).  He leads a team carrying out leading research in computer vision and big-data analysis. His special expertise includes robust statistical fitting, computational geometry and machine learning.

Background

  • (Research) Professor of Computer Science, Edith Cowan University, Jan 2018-
  • (Adjunct) Professor of Computer Science, The University of Adelaide, Dec 2017-
  • Professor of Computer Science, The University of Adelaide, 2008-2017
  • Professor of Electrical and Computer Systems Engineering, Monash University, 2006-2008
  • Associate Professor of Electrical and Computer Systems Engineering, Monash University, 2002-2006
  • Senior Lecturer (Electrical and Computer Systems Engineering), Monash University, 1992-2002
  • Lecturer (Computer Science and Computer Engineering), La Trobe University, 1988-1992

Awards and Recognition

National and International Research Positions

  • Member Australian Research Council College of Experts (2008-2011)
  • Editorial Board of “International Journal of Computer Vision” (2004-2013) (currently on the Honorary Editorial Board)
  • Editorial Board “Pattern Recognition” (Aug 2017 – present)
  • Editorial Board of “IPSJ Transactions on Computer Vision and Applications” (2008- 2013)
  • Editorial Board of “Journal of Mathematical Imaging and Vision” (2007-2010)
  • Editorial Board of “Machine Vision and Applications” (2006- 2008)

Research Areas and Interests

  • Computer Vision (Robot Vision)
  • Image Processing
  • Pattern Recognition
  • Big-Data Analysis
  • Robust Statistics
  • Computational Geometry

Qualifications

  • Doctor of Philosophy, La Trobe University, 1991.

Research Outputs

Journal Articles

  • Xiao, G., Wang, H., Ma, J., Suter, D. (2021). Segmentation by continuous latent semantic analysis for multi-structure model fitting. International Journal of Computer Vision, 1(1), 1-23. https://doi.org/10.1007/s11263-021-01468-6.
  • Le, H., Chin, T., Eriksson, A., Do, T., Suter, D. (2021). Deterministic Approximate Methods for Maximum Consensus Robust Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3), 842-857. https://doi.org/10.1109/TPAMI.2019.2939307.

Conference Publications

Journal Articles

  • Tan, DW., Maybery, MT., Gilani, Z., Alvares, G., Mian, A., Suter, D., Whitehouse, AJ. (2020). A broad autism phenotype expressed in facial morphology. Translational Psychiatry, 10(1), article number 7. https://doi.org/10.1038/s41398-020-0695-z.
  • Muthu, S., Tennakoon, R., Rathnayake, T., Hoseinnezhad, R., Suter, D., Bab-Hadiashar, A. (2020). Motion segmentation of RGB-D sequences: Combining semantic and motion information using statistical inference. IEEE Transactions on Image Processing, 29(2020), 5557-5570. https://doi.org/10.1109/TIP.2020.2984893.

Conference Publications

  • Chen, H., Suter, D., Wu, Q., Wang, H. (2020). End-to-End Learning of Object Motion Estimation from Retinal Events for Event-Based Object Tracking. Proceedings of the AAAI Conference on Artificial Intelligence (10534-10541). AAAI Press. https://doi.org/10.1609/aaai.v34i07.6625.
  • Nguyen Duc Minh, C., Gilani, Z., Islam, S., Suter, D. (2020). Learning Affordance Segmentation: An Investigative Study. Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA) (Article number 9363390). IEEE. https://doi.org/10.1109/DICTA51227.2020.9363390.

Journal Articles

  • Wang, H., Xiao, G., Yan, Y., Suter, D. (2019). Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(3), 697-711. https://doi.org/10.1109/TPAMI.2018.2803173.
  • Xiao, G., Wang, H., Yan, Y., Suter, D. (2019). Superpixel-Guided Two-View Deterministic Geometric Model Fitting. International Journal of Computer Vision, 127(4), 323–339. https://doi.org/10.1007/s11263-018-1100-8.

Conference Publications

  • Truong, G., Gilani, Z., Islam, S., Suter, D. (2019). Fast Point Cloud Registration using Semantic Segmentation. Proceedings of 2019 Digital Image Computing: Techniques and Applications (DICTA) (Article number 8945870). IEEE. https://doi.org/10.1109/DICTA47822.2019.8945870.
  • Lin, S., Xiao, G., Yan, Y., Suter, D., Wang, H. (2019). Hypergraph Optimization for Multi-structural Geometric Model Fitting. Proceedings of the AAAI Conference on Artificial Intelligence (8730-8737). Association for the Advancement of Artificial Intelligence. https://doi.org/https://doi.org/10.1609/aaai.v33i01.33018730.

Conference Publications

  • Cai, Z., Chin, T., Le, H., Suter, D. (2018). Deterministic Consensus Maximization with Biconvex Programming. Proceedings European Conference on Computer Vision (699-714). Springer. https://doi.org/10.1007/978-3-030-01258-8_42.
  • Le, H., Eriksson, A., Milford, M., Do, T., Chin, T., Suter, D. (2018). Non-smooth M-estimator for Maximum Consensus Estimation. 29th British Machine Vision Conference (BMVC) (1-12). British Machine Vision Conference.

Books

Journal Articles

  • Lai , T., Wang, H., Yan, Y., Xiao, G., Suter, D. (2017). Efficient guided hypothesis generation for multi-structure epipolar geometry estimation. Computer Vision and Image Understanding, 154(Jan), 152-165. https://doi.org/10.1016/j.cviu.2016.10.003.
  • Chin, T., Purkait, P., Eriksson, A., Suter, D. (2017). Efficient Globally Optimal Consensus Maximisation with Tree Search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 758-772. https://doi.org/10.1109/TPAMI.2016.2631531.
  • Purkait, P., Chin, T., Sadri, A., Suter, D. (2017). Clustering with Hypergraphs: The Case for Large Hyperedges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(9), 1697-1711. https://doi.org/10.1109/TPAMI.2016.2614980.

Conference Publications

  • Zhang, Q., Chin, T., Suter, D. (2017). Quasiconvex Plane Sweep for Triangulation with Outliers. Proceedings of the IEEE International Conference on Computer Vision (920-928). IEEE. https://doi.org/10.1109/ICCV.2017.105.
  • Le, H., Chin, T., Suter, D. (2017). An Exact Penalty Method for Locally Convergent Maximum Consensus. Proceedings 30th IEEE Conference on Computer Vision and Pattern Recognition (378-387). IEEE. https://doi.org/10.1109/CVPR.2017.48.

Journal Articles

  • Xiao, G., Wang, H., Lai, T., Suter, D. (2016). Hypergraph modelling for geometric model fitting. Pattern Recognition, 60(2016), 748-760. https://doi.org/10.1016/j.patcog.2016.06.026.
  • Tennakoon, R., Bab-Hadiashar, A., Cao, Z., Hoseinnezhad, R., Suter, D. (2016). Robust Model Fitting Using Higher Than Minimal Subset Sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 350-362. https://doi.org/10.1109/TPAMI.2015.2448103.
  • Bustos, P., Chin, T., Eriksson, A., Li, H., Suter, D. (2016). Fast Rotation Search with Stereographic Projections for 3D Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2227-2240. https://doi.org/10.1109/TPAMI.2016.2517636.

Conference Publications

  • Xiao, G., Wang, H., Yan, Y., Suter, D. (2016). Superpixel-based two-view deterministic fitting for multiple-structure data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (517-533). European Conference on Computer Vision. https://doi.org/10.1007/978-3-319-46466-4_31.
  • Le, H., Chin, T., Suter, D. (2016). Conformal Surface Alignment with Optimal Mobius Search. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2507-2516). IEEE. https://doi.org/10.1109/CVPR.2016.275.

Conference Publications

  • Chin, T., Purkait, P., Eriksson, A., Suter, D. (2015). Efficient globally optimal consensus maximisation with tree search. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2413–2421). Institute of Electrical and Electronics Engineers. https://doi.org/https://doi.org/10.1109/CVPR.2015.7298855.
  • Wang, H., Xiao, G., Yan, Y., Suter, D. (2015). Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting. https://doi.org/10.1109/ICCV.2015.332.
  • Hadian-Jazi, M., Bab-Hadiashar, A., Hoseinnezhad, R., Suter, D. (2015). Theoretical analysis of hough transform optimal cell size: Segmentation of nearby lines. https://doi.org/10.1109/IPTA.2015.7367119.

Journal Articles

  • Tran, Q., Chin, T., Chojnacki, W., Suter, D. (2014). Sampling minimal subsets with large spans for robust estimation. International Journal of Computer Vision, 106(1), 93-112. https://doi.org/10.1007/s11263-013-0643-y.
  • Zaragoza, J., Chin, T., Tran, Q., Brown, MS., 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.
  • 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.
  • Pham, TT., Chin, T., 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.
  • Pham, TT., Chin, T., 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.
  • Yu, J., Eriksson, A., Chin, T., Suter, D. (2014). An adversarial optimization approach to efficient outlier removal. Journal of Mathematical Imaging and Vision, 48(3), 451-466. https://doi.org/10.1007/s10851-013-0418-7.

Conference Publications

Research Projects

  • Automated methods for evaluating structural vascular disease, National Health and Medical Research Council, Ideas grants, 2020 ‑ 2024, $652,128.
  • Processing textual input at a system-wide level to elicit insights into individual user characteristics, CingleVue International Pty Ltd, Scholarships to Support Industry Engagement PhD Projects , 2019 ‑ 2024, $64,740.
  • Predicting falls in the elderly: A novel machine learning approach. , Edith Cowan University, Australia-Germany JRC Scheme (UA-DAAD), 2021 ‑ 2023, $24,250.
  • Developing a Framework for Speech Recognition and understanding in digital learning contexts, Science and Industry Endowment Fund, SIEF - Ross Metcalf Stem Business Fellowship, 2020 ‑ 2023, $322,771.
  • Developing a novel deep learning architecture for automatic cardiac arrhythmia detection and classification, Department of Jobs, Tourism, Science and Innovation, WA Science Industry PhD Fellowship Program, 2019 ‑ 2022, $30,000.
  • Tensor and Hypergraph Methods in Fitting Visual Data, Australian Research Council, Grant - Discovery Projects, 2020 ‑ 2022, $485,266.
  • Investigating the opportunities and challenges posed by disruptive and converging technologies in Cyber, IoT, AI/ autonomous technologies to the mission, design, structure, operations and future roles and planning of Defence and the Australian Defence Force, Department of Defence, Strategic Policy Grants Program, 2020 ‑ 2021, $89,045.
  • ECU-Institute Technico Lisboa Collaboration in Artificial Intelligence, Edith Cowan University, ECU Collaboration Enhancement Scheme - 2018 Round 1, 2018 ‑ 2019, $7,672.

Research Student Supervision

Principal Supervisor

  • Doctor of Philosophy, Cardiac arrhythmias classification using deep learning methods
  • Master of Science (Computer Science), Affordance Learning for Visual-Semantic Perception
  • Doctor of Philosophy, Fast point cloud registration using semantic segmentation
  • Doctor of Philosophy, Multi-scenario multimodal sentiment analysis for online learning platform

Associate Supervisor

  • Doctor of Philosophy, Development of a 3D morphable ear model using dense correspondence

Principal Supervisor

  • Doctor of Philosophy: Robust Paramater Estimation in Computer Vision: Geometric Fitting and Deformable Registration
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