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A. Loddo, Putzu, L., Di Ruberto, C., e 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, pagg 112 – 118.
A. Loddo e 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., e 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, pagg 15 – 23.
A. Loddo e Putzu, L., «On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario», AI, vol 2, pagg 394–412, 2021.
C. Lobrano, Tronci, R., Giacinto, G., e Roli, F., «Dynamic Linear Combination of Two-Class Classifiers», in Lecture Notes in Computer Science, 2010, vol 6218, pagg 473-482.
C. Lobrano, Tronci, R., Giacinto, G., e Roli, F., «A Score Decidability Index for Dynamic Score Combination», in Pattern Recognition, International Conference on, Los Alamitos, CA, USA, 2010, pagg 69-72.
H. - Y. Lin e Biggio, B., «Adversarial Machine Learning: Attacks From Laboratories to the Real World», Computer, vol 54, pagg 56-60, 2021.
C. - T. Li e 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 e Satta, R., «Empirical Investigation into the Correlation between Vignetting Effect and the Quality of Sensor Pattern Noise», IET Computer Vision, vol 6, n° 6, pagg 560-566, 2012.
E. Ledda, Putzu, L., Delussu, R., Fumera, G., e 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. pagg 301 – 311, 2022.
E. Ledda, Putzu, L., Delussu, R., Loddo, A., e Fumera, G., «How Realistic Should Synthetic Images Be for Training Crowd Counting Models?», in Computer Analysis of Images and Patterns, Cham, 2021, pagg 46–56.
B. Lavi, Fumera, G., e Roli, F., «Multi-Stage Ranking Approach for Fast Person Re-Identification», IET Computer Vision, vol 12, n° 4, pag 7, 2018. (1.07 MB)
R. Labaca-Castro, Biggio, B., e 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, pagg 2565–2567.