Publications

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C. Di Ruberto e Putzu, L., «Accurate blood cells segmentation through intuitionistic fuzzy set threshold», in Proceedings - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014, 2015, pagg 57 – 64.
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.
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C. Di Ruberto, Fodde, G., e Putzu, L., «Comparison of statistical features for medical colour image classification», Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 9163. pagg 3 – 13, 2015.
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.
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.
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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.
C. Di Ruberto, Fodde, G., e Putzu, L., «On different colour spaces for medical colour image classification», Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 9256, pagg 477 – 488, 2015.
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A. Loddo e Putzu, L., «On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario», AI, vol 2, pagg 394–412, 2021.
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.
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.
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.
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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.
C. Di Ruberto e Putzu, L., «A fast leaf recognition algorithm based on SVM classifier and high dimensional feature vector», in VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications, 2014, vol 1, pagg 601 – 609.
C. Di Ruberto e Putzu, L., A Feature Learning Framework for Histology Images Classification. 2016, pagg 37 – 48.
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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.
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.
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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.
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)
L. Putzu e Di Ruberto, C., «Investigation of different classification models to determine the presence of leukemia in peripheral blood image», in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, vol 8156 LNCS, pagg 612 – 621.
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C. Di Ruberto, Loddo, A., e Putzu, L., «Learning by sampling for white blood cells segmentation», in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol 9279, pagg 557 – 567.
L. Putzu, Caocci, G., e Di Ruberto, C., «Leucocyte classification for leukaemia detection using image processing techniques», Artificial Intelligence in Medicine, vol 62, pagg 179 – 191, 2014.
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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L. Putzu, Di Ruberto, C., e Fenu, G., «A mobile application for leaf detection in complex background using saliency maps», Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 10016 LNCS, pagg 570 – 581, 2016.
C. Di Ruberto, Loddo, A., e Putzu, L., «A multiple classifier learning by sampling system for white blood cells segmentation», Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 9257, pagg 415 – 425, 2015.

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