Dr George Azzopardi

B.Sc.I.T.(Hons)(Lond.),M.Sc.(Lond.),Ph.D.(Groningen)

Lecturer

Artificial Intelligence

Faculty of Information & Communication Technology

+356 2340 2507

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Biography

As of March 2015 I am an Academic Resident (Lecturer) at the Intelligent Computer Systems department of the ICT Faculty in the University of Malta. I am involved in lecturing courses about intelligent interfaces, pattern recognition and computer vision. I am also affiliated with the University of Groningen in the Netherlands where I co-supervise PhD and Masters students.

Together with my collaborators from the Universities of Groningen and Wageningen (the Netherlands), I am the recipient of a ~EUR500k research grant, which we received from the Breed4Food program of STW in the Netherlands. The project is called SMARTBREED - Smart animal breeding using advanced machine learning. More details can be found in here.

Before my current position I was a research innovator at TNO (4 days per week) and a post-doc researcher/lecturer at University of Groningen (one day per week), in the Netherlands. At TNO I was involved in optimization, signal processing, predictive modeling and computer vision projects.

I received a PhD cum laude in Computer Science from University of Groningen (Netherlands) in April 2013. During my studies I developed novel trainable pattern recognition algorithms and published my work on high ranking peer-reviewed journals including IEEE Transactions on Pattern Analysis and Machine Intelligence and Medical Image Analysis. The thesis can be downloaded from http://www.cs.rug.nl/~george/#Downloads.

In 2006 I received a BSc degree (first class honours) in Computer Science and in 2008 I received an MSc degree with distinction in Computer Vision, both from University of London. For the BSc degree I received an academic award for my outstanding achievement and for the MSc degree I ranked first.

Between the year 2000 and 2007, I was a full-time software developer at Bank of Valletta (Malta) and for more than three years I was a scientific developer (part-time) at Iteanova Ltd.

I am open for collaborations with academic and industrial organisations. Feel free to send me an email.

Research Interests

Publications

Azzopardi, G., Strisciuglio, N., Vento, M. & Petkov, N. 2015, "Trainable COSFIRE filters for vessel delineation with application to retinal images", Medical image analysis, vol. 19, no. 1, pp. 46-57.

Azzopardi, G. & Petkov, N. 2014, "Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models.", Front Comput Neurosci, vol. 8, pp. 80.

Azzopardi, G., RodrÃguez-SÃ¡nchez, A., Piater, J. & Petkov, N. 2014, "A Push-Pull CORF Model of a Simple Cell with Antiphase Inhibition Improves SNR and Contour Detection", PLoS ONE, vol. 9, no. 7, pp. e98424.

Azzopardi, G. & Petkov, N. 2014, "COSFIRE: A Brain-Inspired Approach to Visual Pattern Recognition", Lecture Notes in Computer Science, Brain-Inspired Computing
, eds. Gr, L. inetti, T. Lippert & N. Petkov, Springer, , pp. 76-87.

de Vries, H., Azzopardi, G., Koelewijn, A. & Knobbe, A. 2014, "Parametric Nonlinear Regression Models for Dike Monitoring Systems", Lecture Notes in Computer Science, Advances in Intelligent Data Analysis XIII, eds. H. Blockeel, M. van Leeuwen & V. Vinciotti, Springer International Publishing, , pp. 345-355.

Azzopardi, G. & Petkov, N. 2013, "Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters", Pattern Recognition Letters, vol. 34, no. 8, pp. 922-933.

Azzopardi, G. & Petkov, N. 2013, "A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits", Computer Analysis of Images and Patterns (CAIP 2013) Lecture Notes in Computer Science, pp. 9.

Azzopardi, G. & Petkov, N. 2013, "Trainable COSFIRE filters for keypoint detection and pattern recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 2, pp. 490-503.

Azzopardi, G. & Petkov, N. 2012, "A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model", Biological cybernetics, vol. 106, no. 3, pp. 177-189.

Azzopardi, G. & Smeraldi, F. 2009, "Variance Ranklets: orientation-selective rank features for contrast modulations", British Machine Vision Conference (BMVC).

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Dr George Azzopardi