White papers and Magazines:
M. Emambakhsh, A. Deras and R. Turner, "COVID-19, Economy and FX: Past, Present and Future", Mesirow, 2022. PDF
M. Emambakhsh, J. Nichols and R. Turner, "ESG for Systematic FX Trading", Mesirow, 2021. PDF
M. Emambakhsh, "Systematic FX Trading via Micro and Macro Deep Neural Networks", European Pensions, 2021. PDF
M. Emambakhsh and R. Turner, "High Dimensional Data Visualisation for Systematic FX Trading", Mesirow, 2021. PDF
M. Emambakhsh and R. Turner, "Deep neural networks for FX prediction", Mesirow, 2020. PDF
Journals:
P. Mahzari, M. Emambakhsh, C. Temizel, and A. P. Jones, "Oil production forecasting using deep learning for shale oil wells under variable gas-oil and water-oil ratios", Petroleum Science and Technology, 2021 (in press). PDF
M. Emambakhsh, A. Bay, and E. Vazquez, "Convolutional Recurrent Predictor: Implicit Representation for Multi-target Filtering and Tracking", IEEE Transactions on Signal Processing, vol. 67, no. 17, pp. 4545-4555, 2019. PDF
M. Emambakhsh, A. Bay, and E. Vazquez, “Filtering Point Targets via Online Learning of Motion Models,” https://arxiv.org/abs/1902.07630, 2019. PDF
M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017. PDF Code Description
M. Emambakhsh, J. Gao, and A. Evans, “Noise modelling for denoising and 3D face recognition algorithms performance evaluation,” IET Computer Vision, vol. 9, no. 5, pp. 741-749, 2015. PDF
M. Emambakhsh, H. Ebrahimnezhad, and M. Sedaaghi, “Integrated region-based segmentation using color components and texture features with prior shape knowledge,” International Journal of Applied Mathematics and Computer Science (AMCS), vol. 20, no. 4, pp. 711–726, 2010. PDF
Book Chapters:
D. Bhowmik and M. Emambakhsh, “Image Processing for Surveillance and Security,” Handbook of Research on Applied Cybernetics and Systems Science, IGI Global, pp. 52-81, 2017. Available: https://t.co/Ej2TKOL1QL
International Conferences:
A. Ahrabian, M. Emambakhsh, M. Sheeny and A. Wallace, "Efficient Multi-Sensor Extended Target Tracking using GM-PHD Filter", 30th IEEE Intelligent Vehicles (IV) Symposium, Paris, France, pp. 1731-1738, 2019. PDF
M. Emambakhsh, A. Bay, and E. Vazquez, "Deep Recurrent Neural Network for Multi-target Filtering", 25th International Conference, MMM 2019, Thessaloniki, Greece, pp. 519--531, 2018. PDF Presentation Video
M. Sheeny, A. Wallace, M. Emambakhsh, S. Wang and B. Connor, "POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors", Computer Vision and Pattern Recognition Workshop (CVPRW 2018), Salt Lake City, UT, USA, 2018, pp. 1328-13286. PDF Presentation
P. Garcia, M. Emambakhsh, and A. Wallace, “Learning to approximate computing at run-time,” IET 3rd International Conference on Intelligent Signal Processing (ISP 2017), London, UK, 2017, pp.1-6. PDF Code
M. Emambakhsh, Y. He, and I. Nabney, “Machine-printed and handwritten discrimination using a template matching approach,” in IAPR 12th International Workshop on Document Analysis Systems (DAS 2016), 2016, pp. 399-404. PDF Presentation
J. Gao, M. Emambakhsh, and A. Evans, “A low dimensionality expression robust rejector for 3D face recognition,” in IEEE 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden, 2014, pp. 506–511. PDF
M. Emambakhsh, J. Gao, and A. Evans, “An evaluation of denoising algorithms for 3D face recognition,” in IET 5th International Conference on Imaging for Crime Detection and Prevention 2013 (ICDP 2013), London, UK, 2013, pp. 1–6. PDF Presentation
M. Emambakhsh, A. Evans, and M. Smith, “Using nasal curves matching for expression robust 3D nose recognition,” in IEEE 6th International Conference on Biometrics : Theory; Applications and Systems (BTAS 2013), Washington DC, USA, 2013, pp. 1–8. PDF Presentation
M. Emambakhsh and A. Evans, “Self-dependent 3D face rotational alignment using the nose region,” in IET 4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011), London, UK, 2011, pp. 1–6. PDF Presentation
M. Emambakhsh, H. Ebrahimnezhad, and M. Sedaaghi, “A hybrid top-down/bottom-up approach for image segmentation incorporating color and texture with prior shape knowledge,” in IEEE 18th Iranian Conference on Electrical Engineering (ICEE 2010), Isfahan, Iran, 2010, pp. 270–275. PDF Presentation
M. Emambakhsh, M. Sedaaghi, and H. Ebrahimnezhad, “Locating texture boundaries using a fast unsupervised approach based on clustering algorithms fusion and level set,” in IEEE International Conference on Signal and Image Processing Applications (ICSIPA 2009), Kuala Lumpur, Malaysia, 2009, pp. 123–128. PDF Presentation
M. Emambakhsh and M. Sedaaghi, “Automatic MRI brain segmentation using local features, self-organizing maps, and watershed,” in IEEE International Conference on Signal and Image Processing Applications (ICSIPA 2009), Kuala Lumpur, Malaysia, 2009, pp. 129–134. PDF Presentation Description Code
M. Emambakhsh, M. Sedaaghi, and H. Ebrahimnezhad, “A fast unsupervised approach for noisy color image segmentation using clustering algorithms,” in 12th Iranian Student Conference on Electrical Engineering (ISCEE 2009), Tabriz, Iran, 2009, (In Persian). [Online]. Available: http://www.civilica.com/Paper-ISCEE12-ISCEE12_067.html
M. Emambakhsh and H. Ebrahimnezhad, “Texture segmentation using level sets based on color, oriented differentials, and nonlinear diffusion,” in 14th Iranian CSI Computer Conference (CSICC 2009), Tehran, Iran, 2009, (In Persian). [Online]. Available: http://www.civilica.com/Paper-ACCSI14-ACCSI14_123.html
PhD Thesis:
M. Emambakhsh, "Using the 3D shape of the nose for biometric authentication", Thesis for PhD in Electronic and Electrical Engineering, University of Bath, 2014. PDF