Dr John MASHFORD
Senior Fellow (Associate)
School of Mathematics and Statistics
- mashford@unimelb.edu.au
- Find an Expert profile
- Website https://findanexpert.unimelb.edu.au/display/person11242
- Room: 157
- Building: Peter Hall Building
- Campus: Parkville
Recent Publications
J. Mashford, Y Song, Q. Wang, D Robertson. A Bayesian hierarchical spatio-temporal rainfall model. Journal of Applied Statistics, 217-229, 2018. doi: 10.1080/02664763.2018.1473347.
Extra Information
I work principally in the area of mathematical physics. In particular, in the areas of 4D conformal invariant quantum field theory, invariant matrix valued measures and supersymmetry. I also work in the areas of computer vision, statistics and stochastic processes. Some recent publications Mashford, J, "An Approach to Classical Quantum Field Theory Based on the Geometry of Locally Conformally Flat Space-Time," Advances in Mathematical Physics, vol. 2017, Article ID 8070462, 15 pages, 2017. doi:10.1155/2017/8070462. Mashford, J., Second quantized quantum field theory based on invariance properties of locally conformally flat space-times, arXiv:1709.09226. Mashford, J., Lipkin, F., Olie, C. and Cuchennec, M., “Automatic interpretation of remotely sensed images for urban form assessment”, Proc. International Conference on Image Analysis and Recognition ICIAR 2014, Portugal, October 22-24, 2014, Springer Verlag, 441-449. Mashford, J., Rahilly, M., Lane, B., Marney, D. and Burn, S., “Edge detection in pipe images using classification of Haar wavelet transforms”, Applied Artificial Intelligence 28(7), 2014, 675-689. Mashford, J., Marlow, D., Marney, D. and Burn, S., “A mathematical formulation of the problem of optimization of inspection planning in asset management”, Proc. of the Sixth World Congress on Engineering Asset Management, Cincinnati, OH, USA, 3–5 October 2011, USA: Springer, 2014, 485–493. Mashford, J., “Image segmentation using the MCV image labelling algorithm”, Conference: IPCV'13 The 2013 International Conference on Image Processing, Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, 22–25 July 2013, USA: World Academy of Science, 2013, 728–732. Mashford, J., De Silva, D., Burn, S. and Marney, D., “Leak detection in simulated water pipe networks using SVM”, Applied Artificial Intelligence 26, 2012, 429-444.