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A. Loddo and Putzu, L., “On the Reliability of CNNs in Clinical Practice: A Computer-Aided Diagnosis System Case Study”, Applied Sciences (Switzerland), vol. 12, 2022.
A. Loddo, Di Ruberto, C., and Putzu, L., “Peripheral blood image analysis”, in VISIGRAPP 2016 - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Doctoral Consortium, 2016, pp. 15 – 23.
A. Loddo and Putzu, L., “On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario”, AI, vol. 2, pp. 394–412, 2021.
A. Loddo, Putzu, L., Di Ruberto, C., and Fenu, G., “A Computer-Aided System for Differential Count from Peripheral Blood Cell Images”, in Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016, 2017, pp. 112 – 118.
C. Lobrano, Tronci, R., Giacinto, G., and Roli, F., “Dynamic Linear Combination of Two-Class Classifiers”, in Lecture Notes in Computer Science, 2010, vol. 6218, pp. 473-482.
C. Lobrano, Tronci, R., Giacinto, G., and Roli, F., “A Score Decidability Index for Dynamic Score Combination”, in Pattern Recognition, International Conference on, Los Alamitos, CA, USA, 2010, pp. 69-72.
H. - Y. Lin and Biggio, B., “Adversarial Machine Learning: Attacks From Laboratories to the Real World”, Computer, vol. 54, pp. 56-60, 2021.
C. - T. Li and Satta, R., “On the Location-Dependent Quality of the Sensor Pattern Noise and Its Implication in Multimedia Forensics”, in 4th International Conference on Imaging for Crime Detection and Prevention (ICDP 2011), London, United Kingdom, 2011. (399.99 KB)
C. - T. Li and Satta, R., “Empirical Investigation into the Correlation between Vignetting Effect and the Quality of Sensor Pattern Noise”, IET Computer Vision, vol. 6, no. 6, pp. 560-566, 2012.
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.
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.
B. Lavi, Fumera, G., and Roli, F., “Multi-Stage Ranking Approach for Fast Person Re-Identification”, IET Computer Vision, vol. 12, no. 4, p. 7, 2018. (1.07 MB)
R. Labaca-Castro, Biggio, B., and Rodosek, G. Dreo, “Poster: Attacking Malware Classifiers by Crafting Gradient-Attacks That Preserve Functionality”, in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, New York, NY, USA, 2019, pp. 2565–2567.