Publications

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2018
R. Soleymani, Granger, E., and Fumera, G., “Progressive Boosting for Class Imbalance and Its Application to Face Re-Identification”, Expert Systems With Applications, vol. 101, p. 21, 2018. (1.11 MB)
2019
A. Carcangiu, Spano, L. Davide, Fumera, G., and Roli, F., “DEICTIC: a Compositional and Declarative Gesture Description based on Hidden Markov Model”, International Journal of Human-Computer Studies, vol. 122, p. 20, 2019. (1.69 MB)
2020
A. Sotgiu, Demontis, A., Melis, M., Biggio, B., Fumera, G., Feng, X., and Roli, F., “Deep Neural Rejection against Adversarial Examples”, EURASIP Journal on Information Security, vol. 5, 2020.
R. Delussu, Putzu, L., and Fumera, G., “An Empirical Evaluation of Cross-scene Crowd Counting Performance”, in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISAPP, Valletta - Malta, 2020, vol. 4, pp. 373-380. (527.29 KB)
L. Putzu and Fumera, G., “An empirical evaluation of nuclei segmentation from H&E images in a real application scenario”, Applied Sciences (Switzerland), vol. 10, pp. 1-15, 2020.
R. Soleymani, Granger, E., and Fumera, G., “F-Measure Curves: A Tool to Visualize Classifier Performance Under Imbalance”, Pattern Recognition, vol. 100, p. 107146, 2020. (3.15 MB)
R. Delussu, Putzu, L., and Fumera, G., “Investigating Synthetic Data Sets for Crowd Counting in Cross-scene Scenarios”, in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISAPP 2020, Valletta - Malta, 2020, vol. 4, pp. 365-372. (4.23 MB)
R. Delussu, Putzu, L., Fumera, G., and Roli, F., “Online Domain Adaptation for Person Re-Identification with a Human in the Loop”, in 25th International Conference on Pattern Recognition, {ICPR} 2020, Virtual Event / Milan, Italy, January 10-15, 2021, 2020, pp. 3829–3836. (770.02 KB)
2021
L. Putzu, Untesco, M., and Fumera, G., “Automatic Myelofibrosis Grading from Silver-Stained Images”, in Computer Analysis of Images and Patterns, Cham, 2021, pp. 195–205.
E. Ledda, Putzu, L., Delussu, R., Loddo, A., and Fumera, G., “How Realistic Should Synthetic Images Be for Training Crowd Counting Models?”, in Computer Analysis of Images and Patterns, Cham, 2021, pp. 46–56.
2022
R. Delussu, Putzu, L., and Fumera, G., “On the Effectiveness of Synthetic Data Sets for Training Person Re-identification Models”, in Proceedings - International Conference on Pattern Recognition, 2022, vol. 2022-August, pp. 1208 – 1214.
E. Ledda, Putzu, L., Delussu, R., Fumera, G., and Roli, F., “On the Evaluation of Video-Based Crowd Counting Models”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13233 LNCS. pp. 301 – 311, 2022.
R. Delussu, Putzu, L., and Fumera, G., “Scene-specific Crowd Counting Using Synthetic Training Images”, Pattern Recognition, vol. 124, 2022. (3.14 MB)

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