Skip to ContentSkip to Navigation
ResearchBernoulli InstituteDepartmentsInformation Systems

Publications

Publications (from the research database)

2018

Karastoyanova, D., & Pufahl, L. (Accepted/In press). Enhancing Business Process Flexibility by Flexible Batch Processing. In H. Panetto , C. Debruyne , H. Proper , C. Ardagna , D. Roman , & R. Meersman (Eds.), Enhancing Business Process Flexibility by Flexible Batch Processing (pp. 426-444). ( Lecture Notes in Computer Science book series ; Vol. 11229). Cham: Springer. https://doi.org/10.1007/978-3-030-02610-3_24
Karastoyanova, D., & Stage, L. (2018). Towards Collaborative and Reproducible Scientific Experiments on Blockchain. 144-149. Paper presented at CAiSE 2018, Tallinn, Estonia. https://doi.org/10.1007/978-3-319-92898-2_12
Shi, C., Zillikens, D., Schmidt, E., Azzopardi, G., Diercks, G. F. H., Guo, J., ... Petkov, N. (2019). Detection of u-serrated patterns in direct immunofluorescence images of autoimmune bullous diseases by inhibition-augmented COSFIRE filters. International Journal of Medical Informatics, 122, 27-36. https://doi.org/10.1016/j.ijmedinf.2018.11.007
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.
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
Alsahaf, A., Azzopardi, G., Ducro, B., Hanenberg, E., Veerkamp, R., & Petkov, N. (2018). Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest. Journal of Animal Science, 96(12), 4935-4943. [sky359]. https://doi.org/10.1093/jas/sky359
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
Azzopardi, G., Greco, A., Saggese, A., & Vento, M. (2018). Fusion of domain-specific and trainable features for gender recognition from face images. IEEE Access, 6, 24171 - 24183. https://doi.org/10.1109/ACCESS.2018.2823378
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.
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.
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
Spiteri, M., & Azzopardi, G. (2018). Customer Churn Prediction for a Motor Insurance Company. In ICDIM Proceedings, Berlin IEEE.
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.
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"
Marfil, R., Azzopardi, G., Bandera, A., & Miranda Dias, J. M. (Accepted/In press). Guest Editor of the Special Issue COBOT-UHAI on Pattern Recognition Letters: Cooperative and Social Robots: Understanding Human Activities and Intentions. Pattern Recognition Letters.
Neocleous, A., Azzopardi, G., & Dee, M. W. (2018). Identification of Possible Miyake Events using COSFIRE Filters. Abstract from International Radiocarbon Conference, Trondheim, Norway.
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
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).
Neocleous, A., Azzopardi, G., & Dee, M. W. (2018). Signal Processing for the Identification of Miyake Events. Abstract from International Radiocarbon Conference, Trondheim, Norway.
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.
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

2019

Neocleous, A., Azzopardi, G., & Dee, M. W. (2019). Identification of possible D14C anomalies since 14 ka BP: A computational intelligence approach. Science of the Total Environment, 663, 162-169. https://doi.org/10.1016/j.scitotenv.2019.01.251
Alsahaf, A., Azzopardi, G., Hanenberg, E., Ducro, B., Veerkamp, R., & Petkov, N. (2019). Estimation of muscle scores of live pigs using a Kinect camera. IEEE Access, 7, 52238 - 52245. https://doi.org/10.1109/ACCESS.2019.2910986
Neocleous, A., Azzopardi, G., Kuitems, M., Scifo, A., & Dee, M. (2019). Trainable filters for the identification of anomalies in cosmogenic isotope data. IEEE Access, 7, 24585 -24592. https://doi.org/10.1109/ACCESS.2019.2900123
Guo, J., Azzopardi, G., Shi, C., Jansonius, N. M., & Petkov, N. (2019). Automatic determination of vertical cup-to-disc ratio in retinal fundus images for glaucoma screening. IEEE Access, 7, 8527-8541. https://doi.org/10.1109/ACCESS.2018.2890544
Demajo, L. M., Guillaumier, K., & Azzopardi, G. (2019). Age Group Recognition from Face Images using a Fusion of CNN- and COSFIRE-based Features. In APPIS 2019
Tabone, W., Wilkinson, M. H. F., van Gaalen, A., Georgiadis, J. R., & Azzopardi, G. (Accepted/In press). Alpha-Tree Segmentation of Human Anatomical Photographic Imagery. In APPIS2019
Bhole, A., Falzon, O., Biehl, M., & Azzopardi, G. (2019). Automatic identification of Holstein cattle using a non-invasive computer vision approach. In CAIP2019 Springer.
Kind, A., & Azzopardi, G. (2019). Computer-Aided Detection System for Diabetic Retinopathy using Retinal Fundus Images. In Computer Analysis of Images and Patterns
Strisciuglio, N., Azzopardi, G., & Petkov, N. (2019). Robust inhibition-augmented operator for delineation of curvilinear structures. Ieee transactions on image processing.

2017

Fernandez Robles, L., Azzopardi, G., Alegre, E., Petkov, N., & Castejón-Limasa, M. (2017). Identification of milling inserts in situ based on a versatile machine vision system. Journal of Manufacturing Systems, 45, 48-57. https://doi.org/10.1016/j.jmsy.2017.08.002
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
Fernandez Robles, L., Azzopardi, G., Alegre, E., & Petkov, N. (2017). Machine-vision-based identification of broken inserts in edge profile milling heads. Robotics and Computer-Integrated Manufacturing, 44, 276-283. https://doi.org/10.1016/j.rcim.2016.10.004
Gecer, B., Azzopardi, G., & Petkov, N. (2017). Color-blob-based COSFIRE filters for object recognition. Image and vision computing, 57, 165-174. https://doi.org/10.1016/j.imavis.2016.10.006
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
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
Guo, J., Shi, C., Azzopardi, G., & Petkov, N. (2017). Inhibition-augmented COSFIRE model of shape-selective neurons. Ibm journal of research and development, 61(2/3), 1-9. [10]. https://doi.org/10.1147/JRD.2017.2679458

2016

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
Azzopardi, G., & Petkov, N. (2016). Special issue on selected papers from CAIP 2015. Machine Vision and Applications, 27(8), 1115-1115. https://doi.org/10.1007/s00138-016-0813-3
Strisciuglio, N., Azzopardi, G., Vento, M., & Petkov, N. (2016). Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Machine Vision and Applications, 27(8), 1137. https://doi.org/10.1007/s00138-016-0781-7
Guo, J., Shi, C., Azzopardi, G., & Petkov, N. (2016). Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition. Machine Vision and Applications, 1-15. https://doi.org/10.1007/s00138-016-0777-3
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
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.

2015

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
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.
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.
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.
Petkov, N., & Azzopardi, G. (Eds.) (2015). Computer Analysis of Images and Patterns: 16th International Conference, CAIP 2015, Valletta, Malta, September 2-4, 2015 Proceedings, Part I. (Lecture Notes in Computer Science; Vol. 9256). Springer International Publishing. https://doi.org/10.1007/978-3-319-23192-1
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)
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).
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.

2014

Azzopardi, G., Strisciuglio, N., Vento, M., & Petkov, N. (2015). Trainable COSFIRE filters for vessel delineation with application to retinal images. Medical image analysis, 19(1), 46-57. https://doi.org/10.1016/j.media.2014.08.002
Azzopardi, G., & Petkov, N. (2014). Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models. Frontiers in Computational Neuroscience, 8, [80]. https://doi.org/10.3389/fncom.2014.00080
Azzopardi, G., Rodriguez-Sanchez, 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, 9(7), [e98424]. https://doi.org/10.1371/journal.pone.0098424
Azzopardi, G., Sanchez, A. R., Piater, J., & Petkov, N. (2014). A computational model of push-pull inhibition of simple cells with application to contour detection. 163-163. Poster session presented at European Conference on Visual Perception 2014, .
Azzopardi, G., & Petkov, N. (2014). COSFIRE: A brain-inspired approach to visual pattern recognition. In L. Grandinetti, T. Lippert, & N. Petkov (Eds.), Brain-Inspired Computing: International Workshop, BrainComp 2013, Revised Selected PapersInternational Workshop, BrainComp 2013, Revised Selected Papers (Vol. 8603, pp. 76-87). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8603). Springer Verlag. https://doi.org/10.1007/978-3-319-12084-3
Azzopardi, G., de Vries, H., Knobbe, A., & Koelewijn, A. (2014). Parametric nonlinear regression models for dike monitoring systems. In Parametric nonlinear regression models for dike monitoring systems (Vol. 8819, pp. 345-355)
Azzopardi, G., Strisciuglio, N., Vento, M., & Petkov, N. (2014). Vessels delineation in retinal images using COSFIRE filters. In Netherlands Conference on Computer Vision, NCCV 2014 Ermelo, Netherlands.

2013

Azzopardi, G., & Petkov, N. (2013). Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Pattern Recognition Letters, 34(8), 922-933. https://doi.org/10.1016/j.patrec.2012.11.002
Azzopardi, G., & Petkov, N. (2013). Trainable COSFIRE Filters for Keypoint Detection and Pattern Recognition. Ieee transactions on pattern analysis and machine intelligence, 35(2), 490-503. https://doi.org/10.1109/TPAMI.2012.106
Azzopardi, G., & Petkov, N. (2013). A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits. In Lecture Notes in Computer Science (Vol. 8048, pp. 9-16). Springer.
Azzopardi, G. (2013). COSFIRE (Combination of Shifted Filter Responses): A trainable filter approach to visual pattern recognition. Groningen: s.n.

2012

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, 106(3), 177-189. https://doi.org/10.1007/s00422-012-0486-6
Azzopardi, G., & Petkov, N. (2012). Contour Detection by CORF Operator. In Lecture Notes in Computer Science (pp. 395-402). Springer.
Azzopardi, G., & Petkov, N. (2012). CORE: A computational model of a simple cell with application to contour detection. Perception, 41, 99-99.
Azzopardi, G., & Petkov, N. (2012). Detection of Retinal Vascular Bifurcations by Rotation- and Scale-Invariant COSFIRE Filters. In EPRINTS-BOOK-TITLE Springer.
Azzopardi, G., & Petkov, N. (2012). Detection of retinal vascular bifurcations by rotation-, scale- and reflection-invariant COSFIRE filters. In EPRINTS-BOOK-TITLE University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Azzopardi, G., & Petkov, N. (2012). V4-like filters applied to the detection of retinal vascular bifurcations. Perception, 41(3), 365-366.

2011

Azzopardi, G., & Petkov, N. (2011). Detection of retinal vascular bifurcations by rotation-, scale- and reflection-invariant COSFIRE filters. In P. Soda, & F. Tortorella (Eds.), 2012 25TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) NEW YORK: IEEE (The Institute of Electrical and Electronics Engineers).
Azzopardi, G., & Petkov, N. (2011). Detection of Retinal Vascular Bifurcations by Trainable V4-Like Filters. In P. Real, D. DiazPernil, H. MolinaAbril, A. Berciano, & W. Kropatsch (Eds.), COMPUTER ANALYSIS OF IMAGES AND PATTERNS: 14TH INTERNATIONAL CONFERENCE, CAIP 2011, PT I (pp. 451-459). (Lecture Notes in Computer Science; Vol. 6854). BERLIN: Springer.

2009

Azzopardi, G., & Smeraldi, F. (2009). Variance Ranklets: Orientation-selective rank features for contrast modulations. In British Machine Vision Conference
Last modified:31 May 2018 4.40 p.m.