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Professor David Suter

Professor of Computer Science

Contact Information Telephone: +61 8 6304 6591, Email: d.suter@ecu.edu.au, Campus: Joondalup, Room: JO18.417
Staff Member Details
Telephone: +61 8 6304 6591
Email: d.suter@ecu.edu.au
Campus: Joondalup  
Room: JO18.417  

 

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

Recent Research Grants

  • Processing textual input at a system-wide level to elicit insights into individual user characteristics,  CingleVue Pty Ltd,  Scholarships to Support Industry Engagement PhD Project,  2019 - 2024,  $64,740.
  • Tensor and Hypergraph Methods in Fitting Visual Data,  Australian Research Council,  Grant - Discovery Projects,  2020 - 2022,  $639,326.
  • Automated methods for evaluating structural vascular disease,  National Health and Medical Research Council,  Ideas grants,  2020 - 2022,  $652,128.
  • 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,  $179,502.
  • 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 - 2021,  $30,000.
  • ECU-Institute Technico Lisboa Collaboration in Artificial Intelligence,  Edith Cowan University,  ECU Collaboration Enhancement Scheme - 2018 Round 1,  2018 - 2019,  $7,672.

Recent Publications (within the last five years)

Books

  • Chin, T., Suter, D., (2017), The Maximum Consensus Problem: Recent Algorithmic Advances. Synthesis Lectures on Computer Vision., San Rafael, Morgan & Claypool Publishers, DOI: 10.2200/S00757ED1V01Y201702COV011.

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), 1-9, DOI: 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(1), 5557-5570, New York, DOI: 10.1109/TIP.2020.2984893.
  • Le, H., Chin, T., Eriksson, A., Do, T., Suter, D., (2019), Deterministic Approximate Methods for Maximum Consensus Robust Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(1), 1-1, New York, DOI: 10.1109/TPAMI.2019.2939307.
  • 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, IEEE, DOI: 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, Kluwer, DOI: 10.1007/s11263-018-1100-8.
  • 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, DOI: 10.1109/TPAMI.2016.2614980.
  • 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, DOI: 10.1109/TPAMI.2016.2631531.
  • 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, DOI: 10.1016/j.cviu.2016.10.003.
  • Xiao, G., Wang, H., Lai, T., Suter, D., (2016), Hypergraph modelling for geometric model fitting. Pattern Recognition, 60(2016), 748-760, DOI: 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, DOI: 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, DOI: 10.1109/TPAMI.2016.2517636.

Conference Publications

  • 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, 33(January 27 – February 1, 2019), 8730-8737, Palo Alto, United States of America, Association for the Advancement of Artificial Intelligence, DOI: https://doi.org/10.1609/aaai.v33i01.33018730.
  • 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, Online, IEEE, DOI: 10.1109/DICTA47822.2019.8945870.
  • Cai, Z., Chin, T., Le, H., Suter, D., (2018), Deterministic Consensus Maximization with Biconvex Programming. Proceedings European Conference on Computer Vision, 11216(08/09/2018-14/09/2018), 699-714, Cham, Switzerland, Springer, DOI: 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, online, British Machine Vision Conference.
  • 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, New York, United States of America, IEEE, DOI: 10.1109/CVPR.2017.48.
  • 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, DOI: 10.1109/ICCV.2017.105.
  • 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, online, IEEE, DOI: 10.1109/CVPR.2016.275.
  • 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, online, European Conference on Computer Vision, DOI: 10.1007/978-3-319-46466-4_31.
  • 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, online, Institute of Electrical and Electronics Engineers, DOI: https://doi.org/10.1109/CVPR.2015.7298855.
  • Hadian-Jazi, M., Bab-Hadiashar, A., Hoseinnezhad, R., Suter, D., (2015), Theoretical analysis of hough transform optimal cell size: Segmentation of nearby lines., 163-168, DOI: 10.1109/IPTA.2015.7367119.
  • Wang, H., Xiao, G., Yan, Y., Suter, D., (2015), Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting., 2902-2910, DOI: 10.1109/ICCV.2015.332.

Research Student Supervision

Principal Supervisor

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