ICAR-CNR INdAM Research Unit

The ICAR-CNR INdAM Research Unit was established in 2013 through a cooperation agreement between the Istituto Nazionale di Alta Matematica ''Francesco Severi'' (INdAM) and the Istituto di Calcolo e Reti ad Alte Prestazioni of the National Research Council of Italy (ICAR-CNR).

Research topics

- Analysis, design, and implementation of methods, algorithms, and prototype software in high-performance computing environments for multimedia applications, with particular attention to images and image sequences. Applications include the detection of moving objects in RGBD videos, the segmentation of melanomas in dermoscopic images, and the high-throughput screening of stem cells in microscope images.

- 3D reconstrunction and analysis of intranuclear proteins in fluorescence microscopy image sequences.

- Development of GPU algorithms for lineage analysis of yeast cells in phase contrast microscopy images in order to reduce time-to-solution.

- Implementation of computationally efficient Blockchain-based applications in contexts not strictly related to crypto-currencies.

- Development of weighted and weightless neural models (including deep neural networks) for feature extraction and classification in image processing, robotics, and bioinformatics domains.

- Metabolic Network Analysis.


- INdAM/GNCS Section on Numerical Analysis

Laura Antonelli, Research Scientist

Annabella Astorino, Research Scientist

- INdAM/GNCS Section on Fundamentals of Computer Science and Information Systems

Maurizio Giordano, Research Scientist

Mario Rosario Guarracino, Research Scientist

Lucia Maddalena, Research Scientist (ICAR-CNR INdAM R.U. Director)

Ichcha Manipur, Research Fellow

Diego Romano, Research Scientist

Giovanni Schmid, Research Scientist (member till 2015)

Scientific products

Software and datasets available online, produced within the scientific activities of the ICAR-CNR INdAM Research Unit
(for a complete list of products available via INdAM, see here)

- Software for moving object detection in image sequences

-- 3dSOBS+

- Software for skin lesion segmentation in dermoscopic images

-- SDI+

- Dataset for moving object detection in RGBD image sequences

-- SBM-RGBD dataset

- Dataset for background model initialization from image sequences

-- SBMnet
-- SBI

Scientific initiatives sponsored by INdAM/GNCS

- Organization of Schools, Workshops, and Seminars

Workshop Bringing Maths to Life - BMTL 2015, Napoli (Italy), October 19-21, 2015

Scene Background Modeling and Initialization (SBMI2015), Workshop in conjunction with ICIAP 2015, Genova (Italy), September 8th, 2015 [1]

Workshop Bringing Maths to Life - BMTL 2014, Napoli (Italy), October 29th, 2014

17th International Conference on Image Analysis and Processing (ICIAP 2013), Naples, September 9-13, 2013

- Participation to Conferences, Schools, Workshops, and Seminars

Parallel and Distributed Computing for Life Sciences: Algorithms, Methodologies, and Tools (PDCLifeS), Workshop of EURO-PAR 2019, Göttingen, Germany, August 26-27, 2019

The 9th International Conference on Network Analysis, Moskow, Russia, May 18-19, 2019

SIAM Conference on Imaging Science, Bologna, Italy, June 5-8, 2018 [4]

Background learning for detection and tracking from RGBD videos (RGBD2017), Workshop in conjunction with ICIAP 2017, Catania (Italy), September 11th, 2017 [10] [5] [3] [2]

Argonne Training Program on Extreme-Scale Computing (ATPESC), Pheasant Run Resort, Illinois, August 3-15, 2014

Publications acknowledging INdAM/GNCS

[19] I. Manipur, M. Manzo, I. Granata, M. Giordano, L. Maddalena, and M.R. Guarracino, Netpro2vec: a Graph Embedding Framework for Biomedical Applications , in IEEE/ACM Transactions on Computational Biology and Bioinformatics, DOI: 10.1109/TCBB.2021.3078089, to appear.

[18] L. Maddalena, I. Granata, I. Manipur, M. Manzo, and M.R. Guarracino, A Framework Based on Metabolic Networks and Biomedical Images Data to Discriminate Glioma Grades, in X. Ye et al. (Eds.), Biomedical Engineering Systems and Technologies. BIOSTEC 2020. CCIS 1400, Springer, ISBN: 978-3-030-72379-8, DOI: 10.1007/978-3-030-72379-8_9, 165--189, 2021.

[17] I. Manipur, I. Granata, L. Maddalena, and M.R. Guarracino, Network Distances for Weighted Digraphs, in Y. Kochetov et al. (Eds.), Mathematical Optimization Theory and Operations Research, CCIS 1275, Springer Nature Switzerland, ISBN: 978-3-030-58657-7, DOI: 10.1007/978-3-030-58657-7_31, 389-408, 2020.

[16] I. Manipur, I. Granata, L. Maddalena, M.R. Guarracino, Clustering analysis of tumor metabolic networks, BMC Bioinformatics, ISSN: 1471-2105, DOI: 10.1186/s12859-020-03564-9, vol.21, n. 349, 2020.

[15] L. Maddalena, M. Gori, and S.K. Pal, Pattern recognition and beyond: Alfredo Petrosino's scientific results, Pattern Recognition Letters, DOI: 10.1016/j.patrec.2020.07.032, 2020.

[14] I. Granata, M.R. Guarracino, L. Maddalena, I. Manipur, and P.M. Pardalos, On Network Similarities and Their Applications, in R.P. Mondaini (Ed.), Trends in Biomathematics: Modeling Cells, Flows, Epidemics, and the Environment: Selected Works from the BIOMAT Consortium Lectures, Szeged, Hungary, 2019, Springer International Publishing, 23-41, ISBN: 978-3-030-46306-9, DOI: 10.1007/978-3-030-46306-9_3, 2020.

[13] L. Maddalena, I. Granata, I. Manipur, M. Manzo, and M.R. Guarracino, Glioma Grade Classification via Omics Imaging, in F. Soares, A. Fred, and H. Gamboa (Eds.), Proceed. 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC2020), Volume 2: BIOIMAGING, ISBN: 978-989-758-398-8, 82-92, 2020 (Best Paper Award).

[12] I. Granata, M.R. Guarracino, V. Kalyagin, L. Maddalena, I. Manipur, and P. Pardalos, Model simplification for supervised classification of metabolic networks, Annals of Mathematics and Artificial Intelligence 88, Springer, 91-104, 2020.

[11] L. Antonelli, M. R. Guarracino, L. Maddalena, and M. Sangiovanni, Integrating imaging and omics data: A review, Biomedical Signal Processing and Control 52, 264–280, 2019.

[10] L. Maddalena and A. Petrosino, Self-Organizing Background Subtraction Using Color and Depth Data, Multimedia Tools and Applications 78(9), 11927--11948, Springer, 2019.

[9] M. R. Guarracino and L. Maddalena, SDI+: a Novel Algorithm for Segmenting Dermoscopic Images, IEEE Journal of Biomedical and Health Informatics, 23(2), 481-488, 2019.

[8] I. Granata, M.R. Guarracino, V. Kalyagin, L. Maddalena, I. Manipur, and P. Pardalos, Supervised Classification of Metabolic Networks, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 2688-2693, 2018.

[7] I. Manipur, I. Granata, L. Maddalena, K.P. Tripathi, and M.R.Guarracino, Clustering Analysis of Tumor Metabolic Networks, Bioinformatics and Computational Biology Conference (BBCC2018), Naples, November 2018.

[6]L Antonelli, V De Simone, Comparison of minimization methods for nonsmooth image segmentation, Communications in Applied and Industrial Mathematics 9 (1), 68-86, 2018.

[5] L. Maddalena and A. Petrosino, Background Subtraction for Moving Object Detection in RGBD Data: A Survey, J. Imaging, 4(5), 71, 2018.

[4] L. Casalino, M. R. Guarracino, and L. Maddalena, Imaging for High-Throughput Screening of Pluripotent Stem Cells, SIAM Conference on Imaging Science - IS18, Bologna, June 2018.

[3] M. Camplani, L. Maddalena, G. Moyá Alcover, A. Petrosino, and L. Salgado, A Benchmarking Framework for Background Subtraction in RGBD Videos, in Battiato S., Farinella G., Leo M., Gallo G. (eds) New Trends in Image Analysis and Processing <96> ICIAP 2017. Lecture Notes in Computer Science, vol 10590, Springer, DOI: 10.1007/978-3-319-70742-6_21, 219-229, 2017.

[2] L. Maddalena and A. Petrosino, Exploiting Color and Depth for Background Subtraction, in Battiato S., Farinella G., Leo M., Gallo G. (eds) New Trends in Image Analysis and Processing <96> ICIAP 2017. Lecture Notes in Computer Science, vol 10590, Springer, DOI: 10.1007/978-3-319-70742-6_24, 254--265, 2017.

[1] L. Maddalena, A. Petrosino, Towards Benchmarking Scene Background Initialization, in V. Murino et al. (eds), New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops, Lecture Notes in Computer Science, Vol. 9281, Springer International Publishing Switzerland, DOI 10.1007/978-3-319-23222-5_57#, 469<96>476, 2015.

Last update: May 13, 2021