| Video background modeling: recent approaches, issues and our proposed techniques M Shah, JD Deng, BJ Woodford Machine vision and applications 25, 1105-1119, 2014 | 85 | 2014 |
| A Self-adaptive CodeBook (SACB) model for real-time background subtraction M Shah, JD Deng, BJ Woodford Image and Vision Computing 38, 52-64, 2015 | 42 | 2015 |
| XYOLO: A model for real-time object detection in humanoid soccer on low-end hardware D Barry, M Shah, M Keijsers, H Khan, B Hopman 2019 International Conference on Image and Vision Computing New Zealand …, 2019 | 28 | 2019 |
| Designing early warning systems for detecting systemic risk: A case study and discussion M Wever, M Shah, N O’Leary Futures 136, 102882, 2022 | 20 | 2022 |
| Illumination invariant background model using mixture of Gaussians and SURF features M Shah, J Deng, B Woodford Asian Conference on Computer Vision, 308-314, 2012 | 19 | 2012 |
| Enhanced Codebook Model for Real-Time Background Subtraction M Shah, J Deng, B Woodford Neural Information Processing, 449-458, 2011 | 18 | 2011 |
| Localized adaptive learning of Mixture of Gaussians models for background extraction M Shah, J Deng, BJ Woodford Image and Vision Computing New Zealand (IVCNZ), 2010 25th International …, 2010 | 15 | 2010 |
| Optimal job packing, a backfill scheduling optimization for a cluster of workstations SM Hussain Shah, K Qureshi, H Rasheed The Journal of Supercomputing 54 (3), 381-399, 2010 | 13 | 2010 |
| Empirical performance evaluation of schedulers for cluster of workstations K Qureshi, SMH Shah, P Manuel Cluster Computing 14 (2), 101-113, 2011 | 10 | 2011 |
| TCP throughput estimation: A new neural networks model SMH Shah, A ur Rehman, AN Khan, MA Shah 2007 International Conference on Emerging Technologies, 94-98, 2007 | 9 | 2007 |
| Enhancing the mixture of gaussians background model with local matching and local adaptive learning M Shah, JD Deng, BJ Woodford Proceedings of the 27th Conference on Image and Vision Computing New Zealand …, 2012 | 7 | 2012 |
| Automatically localising ROIs in hyperspectral images using background subtraction techniques M Shah, V Cave, M dos Reis 2020 35th International Conference on Image and Vision Computing New Zealand …, 2020 | 6 | 2020 |
| Uncertainty-What is It? DM Wheeler, E Meenken, M Espig, M Sharifi, M Shah, SC Finlay-Smits Online]. Retrieved August 16, 2021, 2020 | 5 | 2020 |
| Understanding and communicating uncertainty in data-rich environments: Towards a transdisciplinary approach M Espig, SC Finlay-Smits, ED Meenken, DM Wheeler, M Sharifi, M Shah Nutrient management in farmed landscapes, 2020 | 4 | 2020 |
| Improving Mixture of Gaussians background model through adaptive learning and Spatio-Temporal voting M Shah, JD Deng, BJ Woodford 2013 IEEE International Conference on Image Processing, 3436-3440, 2013 | 4 | 2013 |
| Towards a transdisciplinary approach to systemic risk detection M Wever, N Wognum, M Shah, N O’Leary, G Onofrei Transdisciplinary Engineering for Resilience: Responding to System …, 2021 | 3 | 2021 |
| Digitalization for Sustainable Agriculture: Enabling Farm Digitalization Through Decentralized Control and Ownership A Romera, G Parry, J Turner, M Espig, M Rogerson, M Shah Sustainable Engineering: Concepts and Practices, 3-20, 2024 | 2 | 2024 |
| Electric Sheep team description paper humanoid league kid-size 2019 D Barry, A Curtis-Black, M Keijsers, M Shah, M Young, H Khan, ... arXiv preprint arXiv:1910.08949, 2019 | 2 | 2019 |
| Managing and Governing Integrated Research Programmes: Lessons from Organizational Studies M Wever, A Romera, N Wognum, M Shah Transdisciplinarity and the Future of Engineering, 546-555, 2022 | 1 | 2022 |
| Transdisciplinary approach to hyper-transparency M Wever, N Wognum, M Shah, M Sharifi, MM Reis Transdisciplinary Engineering for Complex Socio-technical Systems–Real-life …, 2020 | 1 | 2020 |
Jeremiah D. DengUniversity of OtagoVerified email at otago.ac.nz