Publications

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2023
C. Di Ruberto, Loddo, A., and Putzu, L., “On The Potential of Image Moments for Medical Diagnosis”, Journal of Imaging, vol. 9, 2023.
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.
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.
R. Delussu, Putzu, L., and Fumera, G., “Scene-specific Crowd Counting Using Synthetic Training Images”, Pattern Recognition, vol. 124, 2022. (3.14 MB)
C. Di Ruberto, Loddo, A., and Putzu, L., “Special Issue on Image Processing Techniques for Biomedical Applications”, Applied Sciences (Switzerland), vol. 12, 2022.
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.
A. Loddo and Putzu, L., “On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario”, AI, vol. 2, pp. 394–412, 2021.
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.
L. Putzu, Loddo, A., and Di Ruberto, C., “Invariant Moments, Textural and Deep Features for Diagnostic MR and CT Image Retrieval”, in Computer Analysis of Images and Patterns, Cham, 2021, pp. 287–297.
2020
L. Putzu, Piras, L., and Giacinto, G., “Convolutional neural networks for relevance feedback in content based image retrieval: A Content based image retrieval system that exploits convolutional neural networks both for feature extraction and for relevance feedback”, Multimedia Tools and Applications, vol. 79, pp. 26995-27021, 2020.
C. Di Ruberto, Loddo, A., and Putzu, L., “Detection of red and white blood cells from microscopic blood images using a region proposal approach”, Computers in Biology and Medicine, vol. 116, 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. 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)
2019
C. Di Ruberto, Loddo, A., and Putzu, L., “A region proposal approach for cells detection and counting from microscopic blood images”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11752 LNCS, pp. 47 – 58, 2019.
2018
C. Di Ruberto, Putzu, L., and Rodriguez, G., “Fast and accurate computation of orthogonal moments for texture analysis”, Pattern Recognition, vol. 83, pp. 498 – 510, 2018.
L. Putzu, Piras, L., and Giacinto, G., “Ten years of Relevance Score for Content Based Image Retrieval”, in 14th International Conference Machine Learning and Data Mining (MLDM), New York, 2018, vol. 10935.
S. Porcu, Loddo, A., Putzu, L., and Di Ruberto, C., “White blood cells counting via vector field convolution nuclei segmentation”, in VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2018, vol. 4, pp. 227 – 234.
2017
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. Di Ruberto, Loddo, A., and Putzu, L., “Histological image analysis by invariant descriptors”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10484 LNCS, pp. 345 – 356, 2017.
L. Putzu and Di Ruberto, C., “Rotation invariant co-occurrence matrix features”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10484 LNCS, pp. 391 – 401, 2017.
2016
C. Di Ruberto and Putzu, L., A Feature Learning Framework for Histology Images Classification. 2016, pp. 37 – 48.
C. Di Ruberto, Loddo, A., and Putzu, L., “A leucocytes count system from blood smear images: Segmentation and counting of white blood cells based on learning by sampling”, Machine Vision and Applications, vol. 27, pp. 1151 – 1160, 2016.

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