Publications

Journal papers
  1. A. Bhole, S. S. Udmale, O. Falzon, G. Azzopardi, “CORF3D contour maps with application to Holstein cattle recognition using RGB and thermal images”, Expert Systems with Application, in print, 2022
  2. A. Alsahaf, N. Petkov, V. Shenoy, G. Azzopardi, “A framework for feature selection through boosting”, Expert Systems with Applications, vol. 187, 115895, 2022
  3. C. Shi, J. J. Meijer, G. Azzopardi, G. F. H. Diercks, J. Guo, N. Petkov, “Use of convolutional neural networks for the detection of u-serrated patterns in direct immunofluorescence images to facilitate the diagnosis of epidermolysis bullosa acquisita”, Journal of American Pathology, in print, 2021
  4. S. Lovdal, R. van den Hartigh, G. Azzopardi, “Injury Prediction in Competitive Runners with Machine Learning”, International Journal of Sports Physiology and Performance,  doi: https://doi.org/10.1123/ijspp.2020-0518, 2021
  5. D. Chaves, E. Fidalgo, E. Alegre, R. Alaiz-Rodriguez, F. Janez-Martin, G. Azzopardi, “Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications”, Sensors, 20(16), 4491, 2020
  6. E. van der Heide, C. Kamphuis, R. Veerkamp, I. Athanasiadis, M. van Pelt, G. Azzopardi, B. Ducro, “Improving predictive performance on survival in dairy cattle using an ensemble learning approach”, Computers and Electronics in Agriculture, vol. 177, 105675, 2020
  7. X. Wang, J. Ellul and G. Azzopardi, “Elderly fall detection systems: A literature survey”, Frontiers in Robotics and AI, 7:71. doi: 10.3389/frobt.2020.00071, 2020
  8. D. Melotti, K. Heimbach, A. Rodriguez-Sanchez, N. Strisciuglio, and G. Azzopardi, “A robust contour detection operator with combined push-pull inhibition and surround suppression”, Information Sciences, vol. 524, pp. 229-240, 2020
  9. S. Farrugia, J. Ellul, and G. Azzopardi, “Detection of illicit over the Ethereum Blockchain”, Expert Systems with Applications, vol. 150, 113318, 2020
  10. S. Ramachandran, N. Strisciuglio, A. Vinekar, R. John, and G. Azzopardi, “U-COSFIRE filters for vessel tortuosity quantification with application to automated diagnosis of retinopathy of prematurity”, Neural Computing and Applications, 32, 12453–12468 (2020), 2020
  11. N. Strisciuglio, G. Azzopardi, N. Petkov, “Robust Inhibition-augmented operator for delineation of curvilinear structures”, IEEE Transactions on Image Processing, vol. 28 (12), 2019.
  12. A. Alsahaf, G. Azzopardi, B. Ducro, E. Hanenberg, R. F. Veerkamp, N. Petkov, “Estimation of muscle scores of live pigs using a Kinect camera”, IEEE Access, vol. 7, p. 52238 – 52245, 2019.
    [Impact Factor: 3.557] [abstract] [pdf] [bib]
  13. A. Neocleous, G. Azzopardi, M. Kuitems, A. Scifo, M. Dee, “Trainable filters for the identification of anomalies in cosmogenic isotope data”, IEEE Access vol. 7, p. 24585 -24592, 2019.
    [Impact Factor: 3.557] [abstract] [pdf] [bib]
  14. A. Neocleous, G. Azzopardi, M. Dee, “Identification of possible D14C anomalies since 14 ka BP: A computational intelligence approach”, Journal of Science of the Total Environment, vol. 663, pp. 162-169, 2019
  15. J. Guo, G. Azzopardi, C. Shi, N. M. Jansonius, N. Petkov, “Automatic determination of vertical cup-to-disc ratio in retinal fundus images for glaucoma screening”, IEEE Access, vol. 7, pp. 8527-8541, 2019.
    [Impact Factor: 3.557] [abstract] [pdf] [bib]
  16. C. Shi, D. Zillikens, E. Schmidt, G. Azzopardi, G.F.H. Diercksr, J. Guo, J.M. Meijer, M. Jonkman, N. Petkov, “Detection of u-serrated patterns in direct immunofluorescence images of autoimmune bullous diseases by inhibition-augmented COSFIRE filters”, International Journal of Medical Informatics, vol. 122, pp. 27-36, 2019
  17. A. Alsahaf, B. Ducro, E. Hanenberg, R. Veerkamp, G. Azzopardi, N. Petkov, “Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest”, Journal of Animal Science, vol. 96(12), pp. 4935-4943, 2018
  18. G. Azzopardi, A. Greco, A. Saggese, M. Vento, “Fusion of domain-specific and trainable features for gender recognition from face images”, IEEE Access, vol. 6(1), pp.24171-24183, 2018.
    [Impact Factor: 3.557] [abstract] [pdf] [bib]
  19. L. Fernandez Robles, G. Azzopardi, E. Alegre, N. Petkov, M. Castejón-Limasa, “Identification of milling inserts in situ based on a versatile machine vision system” Journal of Manufacturing Systems, vol. 45, pp. 48-57, 2017
  20. J. Guo, C. Shi, G. Azzopardi, N. Petkov, “Inhibition-augmented COSFIRE model of shape-selective neurons” IBM Journal of Research and Development, vol. 61 (2/3), pp. 10:1-10:9, 2017
  21. B. Gecer, G. Azzopardi, and N. Petkov, “Color-blob-based COSFIRE filters for Object Recognition” Image and Vision Computing, vol. 57, pp. 165-174, 2017.
    [Impact Factor: 1.77] [abstract] [pdf] [bib]
  22. L. Fernandez Robles, G. Azzopardi, E. Alegre, and N. Petkov, “Machine-vision-based identification of broken inserts in edge profile milling heads” Robotics and Computer-Integrated Manufacturing, vol. 44, pp. 276-283, 2017.
    [Impact Factor: 2.077] [abstract] [pdf] [bib]
  23. N. Strisciuglio, G. Azzopardi, M. Vento, and N. Petkov, “Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters” Machine Vision and Applications, doi: 10.1007/s00138-016-0781-7, 2016.
    [Impact Factor: 1.351] [abstract] [pdf] [bib]
  24. J. Guo, C. Shi, G. Azzopardi, N. Petkov, “Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition” Machine Vision and Applications, doi: 10.1007/s00138-016-0777-3, 2016.
    [Impact Factor: 1.351] [abstract] [pdf] [bib]
  25. G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov, “Trainable COSFIRE filters for vessel delineation with application to retinal images” Medical Image Analysis, vol. 19 (1), pp. 46-57, 2015.
    [Impact Factor: 3.68] [abstract] [pdf] [bib] Top 1%
  26. G. Azzopardi, and N. Petkov, “Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models” Frontiers in Computational Neuroscience, vol. 8(80), 2014.
    [Impact Factor: 2.5] [abstract] [pdf] [bib]
  27. G. Azzopardi, A. Rodriguez Sanchez, J. Piater and N. Petkov, “A push-pull CORF model of a simple cell with antiphase inhibition improves SNR and contour detection”, PLOS ONE, vol. 9 (7): e98424. doi:10.1371/journal.pone.0098424, 2014.
    [Impact Factor: 3.73] [abstract] [pdf] [bib]
  28. G. Azzopardi and N. Petkov, “Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters”, Pattern Recognition Letters, vol. 34 (8), pp. 922-933, 2013.
    [Impact Factor: 1.06] [abstract] [pdf] [bib] [matlab]
  29. G. Azzopardi and N. Petkov, “Trainable COSFIRE filters for keypoint detection and pattern recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35 (2), pp. 490-503, 2013.
    [Impact Factor: 5.7] [abstract] [pdf] [bib] [matlab]
  30. G. Azzopardi and N. Petkov, “A CORF computational model of a simple cell outperforms the Gabor function model”, Biological Cybernetics, vol. 106 (3), pp. 177-189, 2012
    [Impact Factor: 1.93] [abstract] [pdf] [bib] [matlab]
Chapters in conference proceedings
  1. G. S. Bennabhaktula, J. Antonisse, G. Azzopardi, On improving generalization of CNN-based image classification with delineation maps using the CORF push-pull inhibition operator, the 19th international conference of computer analysis of images and patterns (CAIP), Cyprus, in print, 2021
  2. J. Velasco-Mata, D. Chaves, V. De Mata, M. W. Al-Nabki, E. Fidalgo, E. Alegre and G. Azzopardi, Development of a Hardware Benchmark for Forensic Face Detection Applications, Cybersecurity Research National Conferences (JNIC), INCIBE, in print, 2021
  3. R. Biswas, D. Chaves, F. Jáñez-Martino, P. Blanco-Medina, E. Fidalgo, C. García-Olalla, G. Azzopardi, Reinforcement of age estimation in forensic tools to detect Child Sexual Exploitation Material, Cybersecurity Research National Conferences (JNIC), INCIBE, in print, 2021
  4. Wang X., Talavera E., Karastoyanova D., Azzopardi G. (2021) Fall Detection and Recognition from Egocentric Visual Data: A Case Study. In: Del Bimbo A. et al. (eds) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12661. Springer, Cham.
  5. D. Timmerman, G. S. Bennabhaktula, E. Alegre, G. Azzopardi, “Video Camera Identification from Sensor Pattern Noise with a Constrained ConvNet”, In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2021), pages 417-425, 2020
  6. D. Chirtoaca, J. Ellul, G. Azzopardi, “A framework for creating deployable smart contracts for non-fungible tokens on the Ethereum blockchain”, IEEE DAPPS, Oxford (UK), Apr 2020.
  7. G. S. Bennabhaktula, E. Alegre, D. Karastoyanova, G. Azzopardi, “Matching images with similarity learning by convolutional neural networks that exploit the underlying camera sensor pattern noise”, ICPRAM2020, Valletta (Malta), Feb 2020.
  8. A. Bhole, O. Falzon, M. Biehl, G.Azzopardi, “A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle”, CAIP2019, Salerno (Italy), 2019
  9. A. Kind, G. Azzopardi, “An Explainable AI-Based Computer Aided Detection System for Diabetic Retinopathy Using Retinal Fundus Images”, CAIP2019, Salerno (Italy), 2019
  10. F. Simanjuntak, G. Azzopardi, “Fusion of CNN- and COSFIRE-Based Features with Application to Gender Recognition from Face Images”, Advances in Intelligent Systems and Computing – Proceedings of the 1st International Computer Vision Conference (CVC), Vegas, USA, 2019
  11. W. Tabone, M.H.F. Wilkinson, A.E.J.V. Gaalen, J. Georgiadis, G. Azzopardi, “Alpha-Tree Segmentation of Human Anatomical Photographic Imagery”, Applications of Intelligent Systems – Proceedings of the 2nd International APPIS Conference, Gran Canaria Spain, in print, 2019
  12. L. M. Demajo, K. Guillaumier, G. Azzopardi, “Age Group Recognition from Face Images using a Fusion of CNN- and COSFIRE-based Features”, Applications of Intelligent Systems – Proceedings of the 2nd International APPIS Conference, Gran Canaria Spain, in print, 2019
  13. M. Spiteri, G. Azzopardi, “Customer churn prediction for a motor insurance company”, 6th IWDS, Berlin, ICDIM Proceedings, in print, 2018
  14. N. Strisciuglio, G. Azzopardi, N. Petkov, “Robust curvilinear detection operator”, ECCVW Proceedings, in print, 2018
  15. F. Abadi, J. Ellul, G. Azzopardi, “The Blockchain of Things, Beyond Bitcoin: A Systematic Review”, The 1st International Workshop on Blockchain for the Internet of Things 2018 – 2018 IEEE Blockchain – BIoT, in print, 2018
  16. G. Azzopardi, P. Foggia, A. Greco, A. Saggese, M. Vento, “Gender recognition from face images using trainable shape and colour features”, ICPR, Beijing, in print, 2018
  17. A. Bonnici, D. Bugeja, G, Azzopardi, “Vectorisation of sketches with shadows and shading using COSFIRE filters”, DocEng, Halifax, in print, 2018
  18. A. Bonnici, J. Abela, N. Zammit, G. Azzopardi, “Localisation, Recognition and Expression of Ornaments in Music Scores”, DocEng, Halifax, in print, 2018
  19. A. Alsahaf, G. Azzopardi, B. Ducro, R.F. Veerkamp and N. Petkov, “Predicting Slaughter Weight in Pigs with Regression Tree Ensembles.”, Applications of Intelligent Systems – Proceedings of the 1st International APPIS Conference 2018, Frontiers in Artificial Intelligence and Applications 310 (2018), 1-9 . IOS Press, Amsterdam.
  20. A. Apap, L. Fernández-Robles and G. Azzopardi, “Person Identification with Retinal Fundus Biometric Analysis Using COSFIRE Filters”, Applications of Intelligent Systems – Proceedings of the 1st International APPIS Conference 2018, Frontiers in Artificial Intelligence and Applications 310 (2018), 10-18 . IOS Press, Amsterdam.
  21. J. Buhagiar, N. Strisciuglio, N. Petkov and G. Azzopardi, “Automatic Segmentation of Indoor and Outdoor Scenes from Visual Lifelogging.”, Applications of Intelligent Systems – Proceedings of the 1st International APPIS Conference 2018, Frontiers in Artificial Intelligence and Applications 310 (2018), 194-202 . IOS Press, Amsterdam.
  22. A. Alsahaf, G. Azzopardi, B. Ducro, R. F. Veerkamp, N. Petkov, “Assigning pigs to uniform target weight groups using machine learning”, World Congresson Genetics Applied to Livestock Production (WCGALP), Auckland, New Zealand, 2018
  23. A. Rodriguez-Sanchez, D. Chea, G. Azzopardi, S. Stabinger, “A deep learning approach for detecting and correcting highlights in endoscopic images”, International Conference on Image Processing Theory, Tools and Applications (IPTA), Montreal, Canada, 2017
  24. N. Strisciuglio, G. Azzopardi, N. Petkov, “Curvilinear detection with B-COSFIRE Filters: A case study on crack delineation”, Proceedings of the 17th International Conference on Computer Analysis of Images and Patterns (CAIP), in print, Ystad, Sweden, 2017
  25. G. Azzopardi, Antonio Greco, Alessia Saggese, M. Vento, “Fast gender recognition in videos using a novel descriptor based on the gradient magnitudes of facial landmarks”, 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), in print, Lecce, Italy, 2017
  26. G. Azzopardi, L. Fernández-Robles, E. Alegre, N. Petkov, “Increased generalization capability of trainable COSFIRE filters with application to machine vision”, Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), pp. 3356-3361, 2016
  27. G. Azzopardi, A. Greco, M. Vento: “Gender recognition from face images with trainable COSFIRE filters”, 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Colorado Springs, CO, USA, 2016, pp. 235-241.
    [abstract] [pdf] [bib]
  28. G. Azzopardi, A. Greco, M. Vento: “Gender Recognition from Face Images Using a Fusion of SVM Classifiers”, Proceedings of ICIAR 2016 (Póvoa de Varzim, Portugal), Volume 9730 of the series Lecture Notes in Computer Science, pp 533-538
    [abstract] [pdf] [bib]
  29. C. Shi, J. M. Meijer, J. Guo, G. Azzopardi, M. F. Jonkman, N. Petkov: “Automatic Classification of Serrated Patterns in Direct Immunofluorescence Images”, Autonomous Systems 2015 – Proceedings of the 8th GI Conference, Fortschritt-Berichte VDI, Reihe 10 Nr. 842, (Dusseldorf: VDI Verlag, 2015), pp. 61-69, 2015.
    [abstract] [pdf] [bib]
  30. N. Strisciuglio, M. Vento, G. Azzopardi, N. Petkov: Unsupervised delineation of the vessel tree in retinal fundus images. Computational Vision and Medical Image Processing VIPIMAGE 2015, J. Tavares and R.M. Natal Jorge (Eds.), (CRC Press/Balkema, Taylor and Francis Group; Leiden, NL, 2016) pp 149-155.
    [abstract] [pdf] [bib] Best Paper Award
  31. Henri Bouma, Pieter T. Eendebak, Klamer Schutte, George Azzopardi, Gertjan J. Burghouts, “Incremental concept learning with few training examples and hierarchical classification”, Proc. SPIE, vol. 9652, 2015.
    [abstract] [pdf] [bib]
  32. N. Strisciuglio, G. Azzopardi, M. Vento, N. Petkov, “Multiscale Blood Vessel Delineation Using B-COSFIRE Filters”, Proceedings Part II of CAIP2015, LNCS 9257, pp. 300-312, 2015
    [abstract] [pdf] [bib]
  33. J. Guo, C. Shi, G. Azzopardi, N. Petkov, “Recognition of Architectural and Electrical Symbols by COSFIRE Filters with Inhibition”, Proceedings Part II of CAIP2015, LNCS 9257, pp. 348-358, 2015
    [abstract] [pdf] [bib]
  34. A. C. Neocleous, G. Azzopardi, C. Schizas, N. Petkov,
 “Filter-Based Approach for Ornamentation Detection and Recognition in Singing Folk Music”, Proceedings Part I of CAIP2015, LNCS 9256, pp. 558-569, 2015
    [abstract] [pdf] [bib]
  35. L. Fernández-Robles, G. Azzopardi, E. Alegre, N. Petkov, “Cutting Edge Localisation in an Edge Profile Milling Head”, Proceedings Part II of CAIP2015, LNCS 9257, pp. 336-347, 2015
    [abstract] [pdf] [bib]
  36. C. Shi, J. Guo, G. Azzopardi, J. M. Meijer, M. F. Jonkman, N. Petkov, “Automatic Differentiation of u- and n-serrated Patterns in Direct Immunofluorescence Images”, Proceedings Part I of CAIP2015, LNCS 9256, pp. 513-521, 2015.
    [abstract] [pdf] [bib]
  37. K. Schutte, H. Bouma, J. Schavemaker, L. Daniele, M. Sappelli, G. Koot, P. Eendebak, G. Azzopardi, M. Spitters, M. de Boer, M. Kruithof and Paul Brandt, “Interactive detection of incrementally learned concepts in images”, IEEE/ACM International Workshop on Content-Based Multimedia Indexing (CBMI), 2015.
    [abstract] [pdf] [bib]
  38. G. Azzopardi, N. Petkov, “COSFIRE: A brain-inspired approach to visual pattern recognition”, BrainComp, Cetraro, Italy, Lecture Notes in Computer Science, vol. 8603, 2014
    [abstract] [pdf] [bib]
  39. H. de Vries, G. Azzopardi, A. Knobbe, A. Koelewijn, “Parametric nonlinear regression models for dike monitoring systems”, IDA, Leuven, Belgium, Advances in Intelligent Data Analysis, LNCS, vol. 8819, pp. 345-355, 2014
    [abstract] [pdf] [bib]
  40. H. Bouma, G. Azzopardi, et al., “TNO at TRECVID 2013: Multimedia Event Detection and Instance Search”, Proceedings of TRECVID, Maryland, USA, 2013.
    [abstract] [pdf] [bib]
  41. G. Azzopardi and N. Petkov, “A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits”, Computer Analysis of Images and Patterns (CAIP, York, United Kingdom) Lecture Notes in Computer Science vol. 8048, pp. 9-16, 2013.
    [abstract] [pdf] [bib] [presentation]
  42. G. Azzopardi and N. Petkov, “Contour Detection by CORF Operator”, International Conference on Artifical Neural Networks (ICANN, Lausanne, Switzerland) Lecture Notes in Computer Science, vol. 7552, pp. 395-402, 2012.
    [abstract] [pdf] [bib][presentation]
  43. G. Azzopardi and N. Petkov, “Detection of retinal vascular bifurcations by rotation-, scale- and reflection-invariant COSFIRE filters”, Computer Based Medical System (CBMS, Università Campus Bio-Medico di Roma, Italy) IEEE, pp. 1-4, 2012.
    [abstract] [pdf] [bib]
  44. G. Azzopardi and N. Petkov, “Detection of retinal vascular bifurcations by rotation- and scale-invariant COSFIRE filters”, Computer Analysis of Images and Patterns (ICIAR, Aveiro, Portugal) Lecture Notes in Computer Science vol. 7325, pp. 363-371, 2012.
    [abstract] [pdf] [bib]
  45. G. Azzopardi and N. Petkov, “Detection of Retinal Vascular Bifurcations by Trainable V4-Like Filters”, Computer Analysis of Images and Patterns (CAIP, Seville, Spain), Lecture Notes in Computer Science vol. 6854, 2011, pp. 451-459, 2011.
    [abstract] [pdf] [bib] [poster] [presentation] [ground truth data] [web application]
  46. G. Azzopardi and F. Smeraldi, “Variance Ranklets: orientation-selective rank features for contrast modulations”, in BMVC Queen Mary, London UK, 2009.
    [abstract] [pdf] [bib] [poster] [Wikipedia] [matlab script]
  47. G. Azzopardi and K. P. Camilleri, “Offline Handwritten Signature Verification using Radial Basis Function Neural Networks”, in WICT, Malta, 2008.
    [abstract] [pdf] [bib] [poster] [presentation]
Conferences: poster presentations
  1. A. Alsahaf, G. Azzopardi, N. Petkov, “Estimation of live muscle scores of pigs with RGB-D images and machine learning”, FAIR Data Science for Green Life Sciences, Wageningen, Dec 2018
  2. A. Bhole, M. Biehl, G. Azzopardi, “Automatic identification of Holstein cattle using non-invasive computer vision approach”, FAIR Data Science for Green Life Sciences, Wageningen, Dec 2018
  3. A. Neocleous, G. Azzopardi, M. Dee, “Identification of Possible Miyake Events using COSFIRE Filters”, International Radiocarbon Conference, Trondheim (Norway), 2018
  4. A. Neocleous, G. Azzopardi, M. Dee, “Signal Processing for the Identification of Miyake Events”, International Radiocarbon Conference, Trondheim (Norway), 2018
  5. H. Bouma, P. T. Eendebak, K. Schutte, G. Azzopardi, G. J. Burghouts, “Incremental concept learning with few training examples and hierarchical classification”, SPIE Conference: Optics and photonics for counter terrorism, crime fighting and defence, Toulouse, Sep 2015.
  6. G. Azzopardi and N. Petkov, “A computational model of push-pull inhibition of simple cells with application to contour detection”, ECVP, Belgrade, Serbia, 2014.
    [abstract] [poster]
  7. W. Wijbrandi, E. Lazovik, G. Azzopardi, F. Pierie, “A Decision Support System for Planning of Flexible Biogas Chains”, EU BC&E, Hamburg, Germany, June, 2014.
    [abstract] [poster]
  8. G. Azzopardi, N. Strisciuglio, M. Vento and N. Petkov, “Vessel delineation in retinal images using COSFIRE filters”, NCCV, Ermelo, Netherlands, April, 2014.
    [abstract] [pdf] [presentation]
  9. G. Azzopardi and N. Petkov, “Trainable Filtering Approach to Object Recognition and Localization”, AISTATS, Rejkjavik, Iceland, April, 2014.
    [poster]
  10. G. Azzopardi, “GOOSE: Search on internet of connected sensors”, International workshop on brain-inspired computing, Cetraro, Italy, July 2013.
    [abstract] [presentation]
  11. G. Azzopardi and N. Petkov, “COSFIRE: A trainable features approach to pattern recognition”, Benelearn, Nijmegen, Netherlands, June 2013
    [abstract] [presentation]
  12. G. Azzopardi and N. Petkov, “CORF: A computational model of a simple cell with application to contour detection”, ECVP, Alghero, Sardinia, Italy, Sep 2012.
    [abstract] [poster]
  13. G. Azzopardi and N. Petkov, “A CORF computational model of a simple cell with application to contour detection”, AVA/BMVA Meeting on Biological and Computer Vision, Microsoft Research Center, Cambridge, UK, May 2012
    [abstract] [poster]
  14. G. Azzopardi and N. Petkov, “V4-like filters applied to the detection of retinal vascular bifurcations”, AVA/BMVA Meeting on Biological and Computer Vision, School of Psychology, Cardiff University, Wales, May 2011
    [abstract] [poster]

Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, the Netherlands