Publications

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2023
C. Di Ruberto, Loddo, A., e Putzu, L., «On The Potential of Image Moments for Medical Diagnosis», Journal of Imaging, vol 9, 2023.
2022
R. Delussu, Putzu, L., e 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, pagg 1208 – 1214.
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.
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.
R. Delussu, Putzu, L., e Fumera, G., «Scene-specific Crowd Counting Using Synthetic Training Images», Pattern Recognition, vol 124, 2022. (3.14 MB)
C. Di Ruberto, Loddo, A., e Putzu, L., «Special Issue on Image Processing Techniques for Biomedical Applications», Applied Sciences (Switzerland), vol 12, 2022.
2021
L. Putzu, Untesco, M., e Fumera, G., «Automatic Myelofibrosis Grading from Silver-Stained Images», in Computer Analysis of Images and Patterns, Cham, 2021, pagg 195–205.
A. Loddo e Putzu, L., «On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario», AI, vol 2, pagg 394–412, 2021.
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.
L. Putzu, Loddo, A., e 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, pagg 287–297.
2020
L. Putzu, Piras, L., e 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, pagg 26995-27021, 2020.
C. Di Ruberto, Loddo, A., e 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., e 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, pagg 373-380. (527.29 KB)
L. Putzu e Fumera, G., «An empirical evaluation of nuclei segmentation from H&E images in a real application scenario», Applied Sciences (Switzerland), vol 10, pagg 1-15, 2020.
R. Delussu, Putzu, L., e 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, pagg 365-372. (4.23 MB)
R. Delussu, Putzu, L., Fumera, G., e 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, pagg 3829–3836. (770.02 KB)
2019
C. Di Ruberto, Loddo, A., e 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, pagg 47 – 58, 2019.
2018
C. Di Ruberto, Putzu, L., e Rodriguez, G., «Fast and accurate computation of orthogonal moments for texture analysis», Pattern Recognition, vol 83, pagg 498 – 510, 2018.
L. Putzu, Piras, L., e 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., e 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, pagg 227 – 234.
2017
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.
C. Di Ruberto, Loddo, A., e 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, pagg 345 – 356, 2017.
L. Putzu e 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, pagg 391 – 401, 2017.
2016
C. Di Ruberto e Putzu, L., A Feature Learning Framework for Histology Images Classification. 2016, pagg 37 – 48.
C. Di Ruberto, Loddo, A., e 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, pagg 1151 – 1160, 2016.

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