dr. G. (George) Azzopardi

Tenure Track Assistant Professor

dr. G. (George) Azzopardi
E-mail:
g.azzopardi rug.nl

Research

Research units:

Postal address:
Nijenborgh
9
Groningen
Netherlands
Phone: +31 50 363 3939
  1. 2019
  2. Bhole, A., Falzon, O., Biehl, M., & Azzopardi, G. (2019). A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle. In M. Vento, & G. Percannella (Eds.), Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part II (pp. 108-119). (Lecture Notes in Computer Science; Vol. 11679). Cham: Springer. https://doi.org/10.1007/978-3-030-29891-3_10
  3. Bhole, A., Falzon, O., Biehl, M., & Azzopardi, G. (2019). Automatic identification of Holstein cattle using a non-invasive computer vision approach. In M. Vento, & G. Percanella (Eds.), Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part I (Lecture Notes in Computer Science), ( Lecture Notes in Computer Science book series; Vol. 11678). Springer. https://doi.org/10.1007/978-3-030-29888-3_23
  4. Kind, A., & Azzopardi, G. (2019). Computer-Aided Detection System for Diabetic Retinopathy using Retinal Fundus Images. In M. Vento, & G. Percanella (Eds.), International Conference on Computer Analysis of Images and Patterns (Vol. Part 1, pp. 457-468). (Computer Analysis of Images and Patterns; Vol. 11678). Cham: Springer. https://doi.org/10.1007/978-3-030-29888-3_37
  5. 2018
  6. Bhole, A., Biehl, M., & Azzopardi, G. (2018). Automatic identification of Holstein cattle using non-invasive computer vision approach. Abstract from FAIR Data Science for Green Life Sciences, Wageningen, Netherlands.
  7. Alsahaf, A., Azzopardi, G., Ducro, B., Veerkamp, R., & Petkov, N. (2018). Predicting slaughter age in pigs using random forest regression. In N. Petkov, N. Strisciuglio, & C. M. Travieso-Gonzalez (Eds.), Applications of Intelligent Systems IOS Press. https://doi.org/10.1093/jas/sky359
  8. Azzopardi, G., Foggia, P., Greco, A., Saggese, A., & Vento, M. (2018). Gender recognition from face images using trainable shape and colour features. In International Conference of Pattern Recognition (pp. 1983-1988). IEEE. https://doi.org/10.1109/ICPR.2018.8545771
  9. Alsahaf, A., Ducro, B., Veerkamp, R., Azzopardi, G., & Petkov, N. (2018). Assigning pigs to uniform target weight groups using machine learning: ProceedingsoftheWorldCongressonGeneticsAppliedtoLivestockProduction,11.112. In World Congress on Genetics Applied to Livestock Production (WCGALP) University of New Zealand.
  10. Buhagiar, J., Strisciuglio, N., Petkov, N., & Azzopardi, G. (2018). Automatic Segmentation of Indoor and Outdoor Scenes from Visual Lifelogging. Paper presented at Applications of Intelligent Systems 2018, Las Palmas de Gran Canaria, Spain.
  11. Strisciuglio, N., Azzopardi, G., & Petkov, N. (2018). Brain-inspired robust delineation operator. In European Conference of Computer Vision (ECCV) 2018: Workshop on Brain-inspired computer vision
  12. Spiteri, M., & Azzopardi, G. (2018). Customer Churn Prediction for a Motor Insurance Company. In ICDIM Proceedings, Berlin IEEE.
  13. Alsahaf, A., Azzopardi, G., & Petkov, N. (2018). Estimation of live muscle scores of pigs with RGB-D images and machine learning. Abstract from FAIR Data Science for Green Life Sciences, Wageningen, Netherlands.
  14. Azzopardi, G., & Simanjuntak, F. (2018). Fusion of CNN- and COSFIRE-based features with application to Gender Recognition from Face Images. In Springer series "Advances in Intelligent Systems and Computing"
  15. Bonnici, A., Abela, J., Zammit, N., & Azzopardi, G. (2018). Localisation, Recognition and Expression of Ornaments in Music Scores. In DocEng '18 Proceedings of the ACM Symposium on Document Engineering 2018 [25] ACM Press Digital Library. https://doi.org/10.1145/3209280.3209536
  16. Apap, A., Fernandez Robles, L., & Azzopardi, G. (2018). Retinal Fundus Biometric Analysis using COSFIRE Filters. In Proceedings of the first international APPIS conference, Gran Canaria, Spain (Frontiers of Artificial Intelligence and Applications).
  17. Abadi, F., Ellul, J., & Azzopardi, G. (2018). The Blockchain of Things, Beyond Bitcoin: A Systematic Review. In The 1st International Workshop on Blockchain for the Internet of Things 2018 - 2018 IEEE Blockchain - BIoT IEEE.
  18. Bonnici, A., Bugeja, D., & Azzopardi, G. (2018). Vectorisation of sketches with shadows and shading using COSFIRE filters. In DocEng '18 Proceedings of the ACM Symposium on Document Engineering 2018 [23] New York: ACM Press Digital Library. https://doi.org/10.1145/3209280.3209525
  19. 2017
  20. Azzopardi, G., Fernandez-Robles, L., Alegre, E., & Petkov, N. (2017). Increased generalization capability of trainable COSFIRE filters with application to machine vision. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (pp. 3356-3361). [7900152] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2016.7900152
  21. Rodriguez-Sanchez, A., Chea, D., Azzopardi, G., & Stabinger, S. (2017). A deep learning approach for detecting and correcting highlights in endoscopic images. In Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) IEEE. https://doi.org/10.1109/IPTA.2017.8310082
  22. Strisciuglio, N., Azzopardi, G., & Petkov, N. (2017). Detection of curved lines with B-COSFIRE filters: A case study on crack delineation. In Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings (Vol. 10424 LNCS, pp. 108-120). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10424 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-64689-3_9
  23. 2016
  24. Azzopardi, G., Greco, A., & Vento, M. (2016). Gender recognition from face images with trainable COSFIRE filters. In 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2016 (pp. 235-241). [7738068] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AVSS.2016.7738068
  25. Azzopardi, G., Greco, A., & Vento, M. (2016). Gender recognition from face images using a fusion of SVM classifiers. In Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings (Vol. 9730, pp. 533-538). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9730). Springer Verlag. https://doi.org/10.1007/978-3-319-41501-7_59
  26. Strisciuglio, N., Azzopardi, G., Vento, M., & Petkov, N. (2016). Unsupervised delineation of the vessel tree in retinal fundus images. In Computational Vision and Medical Image Processing VIPIMAGE 2015 (pp. 149-155). Taylor & Francis Group.
  27. 2015
  28. Bouma, H., Eendebak, P. T., Schutte, K., Azzopardi, G., & Burghouts, G. J. (2015). Incremental concept learning with few training examples and hierarchical classification. In Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XI; and Optical Materials and Biomaterials in Security and Defence Systems Technology XII (Vol. 9652). [96520E] SPIE. https://doi.org/10.1117/12.2194438
  29. Fernandez Robles, L., Azzopardi, G., Alegre, E., & Petkov, N. (2015). Cutting Edge Localisation in an Edge Profile Milling Head. In Computer Analysis of Images and Patterns: Lecture Notes in Computer Science (Vol. 9257, pp. 336-347). (Lecture notes in computer science; Vol. 9257). Springer.
  30. Shi, C., Meijer, J., Guo, J., Azzopardi, G., Jonkman, M. F., & Petkov, N. (2015). Automatic Classification of Serrated Patterns in Direct Immunofluorescence Images. In Autonomous Systems 2015 - Proceedings of the 8th GI Conference (pp. 61-69). (Fortschritt-Berichte VDI. Informatik/Kommunikationstechnik; Vol. 842). VDI Verlag.
  31. Shi, C., Guo, J., Azzopardi, G., Meijer, J., Jonkman, M. F., & Petkov, N. (2015). Automatic differentiation of u- and n-serrated patterns in direct immunofluorescence images. In Computer Analysis of Images and Patterns (Vol. 9256, pp. 513-521). (Lecture Notes in Computer Science). Springer.
  32. Neocleous, A., Azzopardi, G., Schizas, C., & Petkov, N. (2015). Filter-Based Approach for Ornamentation Detection and Recognition in Singing Folk Music. In Computer Analysis of Images and Patterns (Vol. 9256, pp. 558-569)
  33. Strisciuglio, N., Azzopardi, G., Vento, M., & Petkov, N. (2015). Multiscale Blood Vessel Delineation Using B-COSFIRE Filters. In G. Azzopardi, & N. Petkov (Eds.), Computer Analysis of Images and Patterns (Vol. 9257, pp. 300-12). (Lecture Notes in Computer Science; Vol. 9257).
  34. Guo, J., Shi, C., Azzopardi, G., & Petkov, N. (2015). Recognition of architectural and electrical symbols by COSFIRE filters with inhibition. In Computer Analysis of Images and Patterns (Vol. 9257, pp. 348-358). (Lecture Notes in Computer Science). Springer.
  35. 2014
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