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DE STEFANO CLAUDIO - Professore Ordinario

Italian version

Department: Dipartimento: Ingegneria Elettrica e dell'Informazione "Maurizio Scarano"

Scientific Sector: ING-INF/05

Student reception: Lunedì, ore 15:00 - 17:00

Contact info:
Telefono ufficio: 0776 2993889

  • Teaching FONDAMENTI DI INFORMATICA (92368)

    Primo anno di Ingegneria industriale CASSINO (L-9), Meccanica
    Credits (CFU): 3,00

    Program:
    Introduction to computer programming.
    The concept of algorithm. The Von Neumann model. CPU and main memory organization. Encoding and data representation techniques. Formal languages. Properties of high level programming languages. Compilers and interpreters. The phases of program coding, program compiling and program testing.

    Data types and data structures.
    The concept of information typing. Basic data types: simple and structured data types. Abstract data type: definition of both range of values and allowed operations.

    Control instructions and the object oriented programming model.
    Instruction types: sequence structures, conditional structures and iteration structures. The concept of class of objects: interface and implementation. Object data structure and member functions. Inheritance and polymorphism.

    Methods and techniques for designing programs.
    The main parts of a program: code description, variable definition and instructions. The concept of function: local variables and formal parameters. Data exchange between functions. Effective and formal parameters. The scope of variables. Modular programming. Description and use of standard libraries.

    Basic algorithms and their use in program coding.
    Management of mono-dimensional array: search, deletion, insertion, sorting and merge. Management of bi-dimensional array: search, deletion, insertion, product between matrices. Management of lists. Exercises and applications.

    Databases.
    Basic properties. The relational data model. The Entity-Relationship (ER) model. Structured Query Language (SQL). SQL and programming languages. Exercises and applications.

    Reference books:
    H. Schildt, “Guida al C++”, McGraw-Hill, 3/ed.
    W. Savitch, Programmazione di base e avanzata con JAVA, 2/Ed., Pearson.
    A. Chianese, V. Moscato, A. Picariello, L. Sansone, “Basi di dati per la gestione dell’informazione”, McGraw-Hill.

  • Teaching FONDAMENTI DI INFORMATICA (92368)

    Primo anno di Ingegneria industriale CASSINO (L-9), Elettrica
    Credits (CFU): 6,00

    Program:
    Introduction to computer programming.
    The concept of algorithm. The Von Neumann model. CPU and main memory organization. Encoding and data representation techniques. Formal languages. Properties of high level programming languages. Compilers and interpreters. The phases of program coding, program compiling and program testing.

    Data types and data structures.
    The concept of information typing. Basic data types: simple and structured data types. Abstract data type: definition of both range of values and allowed operations.

    Control instructions and the object oriented programming model.
    Instruction types: sequence structures, conditional structures and iteration structures. The concept of class of objects: interface and implementation. Object data structure and member functions. Inheritance and polymorphism.

    Methods and techniques for designing programs.
    The main parts of a program: code description, variable definition and instructions. The concept of function: local variables and formal parameters. Data exchange between functions. Effective and formal parameters. The scope of variables. Modular programming. Description and use of standard libraries.

    Basic algorithms and their use in program coding.
    Management of mono-dimensional array: search, deletion, insertion, sorting and merge. Management of bi-dimensional array: search, deletion, insertion, product between matrices. Management of lists. Exercises and applications.

    Databases.
    Basic properties. The relational data model. The Entity-Relationship (ER) model. Structured Query Language (SQL). SQL and programming languages. Exercises and applications.

    Reference books:
    H. Schildt, “Guida al C++”, McGraw-Hill, 3/ed.
    W. Savitch, Programmazione di base e avanzata con JAVA, 2/Ed., Pearson.
    A. Chianese, V. Moscato, A. Picariello, L. Sansone, “Basi di dati per la gestione dell’informazione”, McGraw-Hill.

  • Teaching FONDAMENTI DI INFORMATICA (92368)

    Primo anno di Ingegneria industriale CASSINO (L-9), Meccanica
    Credits (CFU): 6,00

    Program:
    Introduction to computer programming.
    The concept of algorithm. The Von Neumann model. CPU and main memory organization. Encoding and data representation techniques. Formal languages. Properties of high level programming languages. Compilers and interpreters. The phases of program coding, program compiling and program testing.

    Data types and data structures.
    The concept of information typing. Basic data types: simple and structured data types. Abstract data type: definition of both range of values and allowed operations.

    Control instructions and the object oriented programming model.
    Instruction types: sequence structures, conditional structures and iteration structures. The concept of class of objects: interface and implementation. Object data structure and member functions. Inheritance and polymorphism.

    Methods and techniques for designing programs.
    The main parts of a program: code description, variable definition and instructions. The concept of function: local variables and formal parameters. Data exchange between functions. Effective and formal parameters. The scope of variables. Modular programming. Description and use of standard libraries.

    Basic algorithms and their use in program coding.
    Management of mono-dimensional array: search, deletion, insertion, sorting and merge. Management of bi-dimensional array: search, deletion, insertion, product between matrices. Management of lists. Exercises and applications.

    Databases.
    Basic properties. The relational data model. The Entity-Relationship (ER) model. Structured Query Language (SQL). SQL and programming languages. Exercises and applications.

    Reference books:
    H. Schildt, “Guida al C++”, McGraw-Hill, 3/ed.
    W. Savitch, Programmazione di base e avanzata con JAVA, 2/Ed., Pearson.
    A. Chianese, V. Moscato, A. Picariello, L. Sansone, “Basi di dati per la gestione dell’informazione”, McGraw-Hill.

  • Teaching FONDAMENTI DI INFORMATICA (92368)

    Primo anno di Ingegneria industriale CASSINO (L-9), Elettrica
    Credits (CFU): 3,00

    Program:
    Introduction to computer programming.
    The concept of algorithm. The Von Neumann model. CPU and main memory organization. Encoding and data representation techniques. Formal languages. Properties of high level programming languages. Compilers and interpreters. The phases of program coding, program compiling and program testing.

    Data types and data structures.
    The concept of information typing. Basic data types: simple and structured data types. Abstract data type: definition of both range of values and allowed operations.

    Control instructions and the object oriented programming model.
    Instruction types: sequence structures, conditional structures and iteration structures. The concept of class of objects: interface and implementation. Object data structure and member functions. Inheritance and polymorphism.

    Methods and techniques for designing programs.
    The main parts of a program: code description, variable definition and instructions. The concept of function: local variables and formal parameters. Data exchange between functions. Effective and formal parameters. The scope of variables. Modular programming. Description and use of standard libraries.

    Basic algorithms and their use in program coding.
    Management of mono-dimensional array: search, deletion, insertion, sorting and merge. Management of bi-dimensional array: search, deletion, insertion, product between matrices. Management of lists. Exercises and applications.

    Databases.
    Basic properties. The relational data model. The Entity-Relationship (ER) model. Structured Query Language (SQL). SQL and programming languages. Exercises and applications.

    Reference books:
    H. Schildt, “Guida al C++”, McGraw-Hill, 3/ed.
    W. Savitch, Programmazione di base e avanzata con JAVA, 2/Ed., Pearson.
    A. Chianese, V. Moscato, A. Picariello, L. Sansone, “Basi di dati per la gestione dell’informazione”, McGraw-Hill.

  • Teaching INTELLIGENZA ARTIFICIALE (30321)

    Secondo anno di Ingegneria Informatica (LM-32), Generale
    Credits (CFU): 9,00

    Program:
    Problem solving.
    Introduction to Artificial Intelligence. Strong and weak thesis in Artificial Intelligence. Limits of the procedural approach to problem solution. Non-procedural models for problem solution. Formal systems. Consistency and completeness of formal systems. State Space Representation. Decomposition in sub-problems. Uninformed search algorithms. Breadth-first and Depth-first search. Informed search algorithms. A* algorithms. Heuristic search.

    Knowledge Representation.
    Predicate calculus. Clauses. Resolution. Introduction to logic programming and Prolog language.

    Uncertain Knowledge Representation.
    A-priori probability and conditional probability. Bayes Rule. Bayesian networks. Fuzzy logic.

    Neural Networks.
    Neural Networks Architectures. Learning models. Single-level perceptron. Multi-level perceptron. Delta Rule. Back-propagation algorithm. Learning Vector Quantization networks. Probabilistic Neural Networks. Brief summary of other network architectures.

    Evolutionary Algorithms.
    Basic aspects of Evolutionary Algorithms. Generation of the initial population. Fitness function. Techniques for selecting individuals. Crossover and mutation operator. Holland’s theorem. Convergence of the evolutionary process. Basic genetic algorithms.

    Learning.
    Automatic learning. Inductive learning: decision tree. Reinforcement learning. Learning and knowledge. Learning through exploration. Feature extraction and representation. Classification. Reliability evaluation. Techniques for combining the results of many classifiers.

    Reference books:
    Stuart Russell e Peter Norvig. Intelligenza artificiale: un approccio moderno, terza edizione, Pearson, Prentice Hall, 2010.
    N. J. Nilsson. Intelligenza Artificiale, Apogeo 2002.
    Lecture notes provided during the course.

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Claudio De Stefano received the Laurea Degree cum laude in Electronic Engineering in 1990 from the University of Naples "Federico II", Italy. At the same university, he attended the Ph.D. Course in Electronic and Computer Engineering and received the Ph.D. Degree in 1994.
He was qualified to practice as an engineer in the second session of qualifying exams in 1990 at the University of Naples “Federico II”, with a score of 120/120.
From July 1990 to March 1991, he received a grant from IBM SEMEA for research activities on Geographic Information Systems in the context of a research project involving both IBM SEMEA and the Department of Information Engineering and Systems of the University of Naples “Federico II”.
In 1994 he received a grant from the Department of Information Engineering and Systems of the University of Naples “Federico II” for research activities on document analysis.
In 1996 he received a grant from the Department of Information Engineering and Systems of the University of Naples “Federico II” for research activities on automatic on-line character recognition.
From October 1996 to October 2001, he has held a Researcher position at the School of Engineering of the University of Sannio, Benevento, Italy.
From November 2001 to January 2012, he has been with the Dipartimento di Automazione, Elettromagnetismo, Ingegneria dell'Informazione e Matematica Industriale (DAEIMI) of the University of Cassino, where he was Associate Professor of Computer Science.
From February 2012 to February 2020, he has been with the Dipartimento di Ingegneria Elettrica e dell’Informazione (DIEI) of the University of Cassino and Southern Lazio, where he was Associate Professor of Computer Science.
He is currently Full Professor of Computer Science at the Dipartimento di Ingegneria Elettrica e dell’Informazione (DIEI) of the University of Cassino and Southern Lazio.
He is the head of the research group on "Pattern Recognition and Artificial Intelligence" at the Dipartimento di Ingegneria Elettrica e dell’Informazione (DIEI) of the University of Cassino and Southern Lazio.
He has been a member of the Teaching Body of the Ph. D. Course in Electrical and Telecommunication Engineering of the University of Cassino and Southern Lazio.
He is currently a member of the Teaching Body of the Ph. D. Course in Methods, Models and Technologies for Engineering of the University of Cassino and Southern Lazio.
He is the head of the Big Data laboratory of the University of Cassino and Southern Lazio, which is one the nodes of the National Big Data laboratory of CINI (Consorzio Interuniversitario Nazionale per l’Informatica).
He is the head of the Laboratory of Artificial Intelligence and Intelligent Systems of the University of Cassino and Southern Lazio, which is one the nodes of the the National Laboratory of Artificial Intelligence and Intelligent Systems (AIIS) of CINI (Consorzio Interuniversitario Nazionale per l’Informatica).
He is member of the Board of Directors of CINI (Consorzio Interuniversitario Nazionale per l’Informatica).
He has been the scientific coordinator of many research projects involving public and private companies, as well as public institutions. In particular:
- he has been the scientific coordinator of the MURST60% projects at the University of Cassino, in the field of pattern recognition;
- he has been the scientific coordinator of a research project involving both the Dipartimento di Automazione, Elettromagnetismo Ingegneria dell’informazione e Matematica Industriale (DAEIMI) of the University of Cassino and the private company Recogniform S.p.A., whose title was: “Off-line recognition of cursive handwriting”;
- he has been the scientific coordinator of a research grant, financed by the private company ELSAG DATAMAT S.p.A., for research activities on the topic: “classification of cursive handwriting”;
- he has been the scientific coordinator of the research project “Unicas-Arti - Una piattaforma digitale per l'arte contemporanea e l'archeologia industriale nel Lazio meridionale”, financed by the Regione Lazio.
- he has been the scientific coordinator of the research unit of the University of Cassino and Southern Lazio for the project “Handwriting Analysis against Neuromuscular Disease - HAND”, funded under the PRIN 2015 call.
From 2002 he is a member of the Associazione Italiana di Intelligenza Artificiale (AI*IA).
From 1992 he is a member of the Italian Association for Research in Computer Vision, Pattern recognition and Machine Learning (CVPL - formerly GIRPR - Italian Group of Researchers in Pattern Recognition).
From 1992 he is a member of the International Association for Pattern Recognition (IAPR).
Since 1998, he has been a member of the International Graphonomics Society (IGS) and, from 2005 to 2010, he was a member of its Governing Board. The International Graphonomics Society (IGS), an international association established in 1985 with headquarters in the United States, has become the leading international reference for scientific research in the field of handwriting analysis, whose applications include neurological basis for understanding human performance, brain modelling, human-machine interfaces, signature verification, handwriting recognition, document processing, healthcare and Digital Paleography.
Since 2017 he is the President of the International Graphonomics Society (IGS).
Claudio De Stefano has been invited as visiting professor in many international research centres, such as the Image Analysis Lab, Department of Computer Science, State University of New York, Stony Brook (USA), the Document Analysis Lab, Department of Electrical, Computer and System Engineering, Rensselaer Polytechnic Institute, Troy, NY and the Adaptive Computation Laboratory at the University of New Mexico, Albuquerque (USA), where he has given invited lectures and seminars.
Over the years, he has participated, as a speaker, in relevant national and international conferences, and he has authored over 150 publications in international journals and congresses. The results of his research activity have been published in relevant international journals, such as: IEEE Transaction on Evolutionary Computation, IEEE Transactions on Systems, Man and Cybernetics, IEEE Transactions on Neural Networks, Pattern Recognition, Pattern Recognition Letters, Information Sciences.
He joined the Program Committees of many important international conferences on image analysis, pattern recognition, handwriting analyses and recognition, such as the International Conference on Frontiers in Handwriting Recognition (ICFHR), the International Conference on Document Analysis and Recognition (ICDAR), the International Conference of Graphonomics Society (IGS), the International Conference on Image Analysis and Processing (ICIAP) and the International Conference on Pattern recognition (ICPR).
He was co-chairman of the international conference IGS2005, the 12th Conference of the International Graphonomics Society.
He was invited to give a tutorial entitled "Evolutionary Algorithms for Pattern Recognition", as part of the ICPR 2006 international conference, the 18th International Conference on Pattern Recognition, Hong Kong.
He was Area Chair (Track 2: Pattern Recognition and Machine Learning) in the organization of the ICPR 2014 international conference (Stockholm, Sweden).
He was the chairman of the international conference IGS2017, the 18th Conference of the International Graphonomics Society.
He has served as referee for many international journals, such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Geoscience and Remote Sensing, International Journal on Document Analysis and Recognition, Pattern Recognition and Pattern Recognition Letters.
He also co-edited books as proceedings of international conferences and special issues of international journals:
- he was Co-Editor of the book "Advances in Graphonomics", Zona Editrice publisher, June 2005,
ISBN: 88-89702-13-3;
- he was Guest Editor of the special issue published on the International Journal of Pattern Recognition and Artificial Intelligence, vol. 21, n.1, February 2007, ISSN: 0218-0014 (https://www.worldscientific.com/toc/ijprai/21/01);
- he was Co-Editor of the book " Graphonomics for e-Citizens: e-Health, e-Society, e-Education ", Zona Editrice publisher, June 2017, ISBN: 9788864387062;
- he was Guest Editor of the special issue published on Pattern Recognition Letters journal entitled “Graphonomics for e-citizens: e-health, e-society, e-education”, April 2019, (https://www.sciencedirect.com/journal/pattern-recognition-letters/vol/121).
- he was Guest Editor of the special issue published on Frontiers in Human Neuroscience journal entitled “Graphonomics and Your Brain on Art, Creativity, and Innovation”, 2020, (https://www.frontiersin.org/research-topics/12060/graphonomics-and-your-brain-on-art-creativity-and-innovation)
He is Associate Editor of the journal Pattern Recognition Letters.
Claudio De Stefano has won the national competition to obtain the qualification as a full professor for the disciplinary scientific field 09/H1 - Information Processing Systems (2013).
Claudio De Stefano has won the national competition to obtain the qualification as a full professor for the disciplinary scientific field 01/B1 - Computer Science (2019).
Claudio De Stefano has won the national competition to obtain the qualification as a full professor for the disciplinary scientific field 09/H1 - Information Processing Systems (2020).
Claudio De Stefano is one of the co-founders of the company Nite s.r.l (Natural Intelligent Technologies s.r.l.), which is a university spin-off at the University of Salerno (2012).
Claudio De Stefano is one of the owners of the following patent: “Procedimento e apparato di riconoscimento di scrittura a mano”, 0001416028 - Natural Intelligent Technologies s.r.l. (2014).
This patent has been extended internationally (United States) with the following name: "Process of Handwriting Recognition and Related Apparatus", Number: 9665768 - Natural Intelligent Technologies s.r.l. (2017).
This patent has also been extended to a European level, Number: 2943911 - Natural Intelligent Technologies s.r.l. (2020).

The scientific interests of Claudio De Stefano include Artificial Vision, Image Processing, Pattern Recognition and Evolutionary Computation. In particular, he has been active in the fields of document analysis and recognition, automatic analysis of cadastral and geographic maps, decomposition and description of digital curves and classification systems based on both neural networks and statistical learning paradigms.
His current research interests include techniques for automatic segmentation and classification of on-line and off-line cursive handwriting, segmentation and classification of images in the context of remote sensing applications, automatic prototype generation methods based on the use of evolutionary algorithms and classifier combination. As regard this last topic, original techniques exploiting the dynamic selection of the classifier have been developed in order to increase performance and reliability of classification systems and a new approach has been proposed based on the use of Bayesian networks. Particular attention was also given to the "feature selection" problem, for which various algorithms were developed and published.
Moreover, in the field of graph based applications, a new evolutionary computation based approach has been developed. This approach has been specifically devised for automatically generating single graphs, or sets of graphs, representing problem solutions. It is based on a special data structure, denoted as multilist, which allows to encode any type of graph, directed or undirected, with or without attributes. Graph encoding by multilists makes it possible to define effective crossover and mutation operators, overcoming the problems normally encountered when implementing genetic operators on graphs. Further advantages of the proposed approach are that it does not require any problem specific knowledge and it is able to search for graphs whose number of nodes is not a priori known.
Writing analysis techniques have recently been applied for the early detection of cognitive disorders. In this context, specific features have been defined that characterize both the graphic gesture and the dynamic handwriting parameters. A process of data acquisition of patients suffering from cognitive disorders at various levels of severity has been started in collaboration with important hospitals. For the data acquisition phase, a specific protocol has been proposed, which includes different writing tasks: the purpose of these tasks is to evaluate the alterations of writing performance in relation to both cognitive and neuro-motor disorders.
Another research activity recently developed concerned the use of writing analysis techniques in the field of digital paleography. In this context, a system for identifying the different scribes who participated in the process of writing a medieval book was proposed. The proposed system receives as input the images of the individual pages of the manuscript to be processed and identifies the parts of text produced by each scribe. Different techniques of extraction and selection of features have been experimented and different classification techniques have been used: the most interesting results were obtained by combining the answers of several cooperating classifiers (multi-expert systems) and with the use of Deep Learning techniques.

2020
1. N.D. Cilia, C. De Stefano, F. Fontanella, C. Marrocco, M. Molinara, A. Scotto di Freca, “An Experimental Comparison between Deep Learning and Classical Machine Learning Approaches for Writer Identification in Medieval Documents, (2020). Journal of Imaging, 6 (9), 89.
2. De Stefano, C., Ferrigno, L., Fontanella, F., Gerevini, L., Scotto di Freca, A., A novel PCA-based approach for building on-board sensor classifiers for water contaminant detection, (2020). Pattern Recognition Letters, 135, pp. 375-381. DOI: 10.1016/j.patrec.2020.05.015.
3. Cilia, N.D., De Stefano, C., Fontanella, F., Scotto di Freca, A., Using Genetic Algorithms for the Prediction of Cognitive Impairments (2020). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12104 LNCS, pp. 479-493. DOI: 10.1007/978-3-030-43722-0_31.
4. Cilia, N.D., De Stefano, C., Fontanella, F., Marrocco, C., Molinara, M., Scotto di Freca, A., An end-to-end deep learning system for medieval writer identification (2020). Pattern Recognition Letters, 29, pp. 137 143. DOI: 10.1016/j.patrec.2019.11.025.
5. Cilia, N.D., De Stefano, C., Fontanella, F., Molinara, M., Scotto di Freca, A., What is the minimum training data size to reliably identify writers in medieval manuscripts? (2020). Pattern Recognition Letters, 29, pp. 198 204. DOI: 10.1016/j.patrec.2019.11.030.
2019
6. De Stefano, C., Fontanella, F., Marcelli, A., Plamondon, R., Graphonomics for the e-citizens: e-health, e-society and e-education (2019). Pattern Recognition Letters, 121, pp. 1-5.
7. Cilia, N.D., De Stefano, C., Fontanella, F., Scotto di Freca, A., A ranking-based feature selection approach for handwritten character recognition (2019). Pattern Recognition Letters, 121, pp. 77-86.
8. De Stefano, C., Fontanella, F., Impedovo, D., Pirlo, G., Scotto di Freca, A., Handwriting analysis to support neurodegenerative diseases diagnosis: A review (2019). Pattern Recognition Letters, 121, pp. 37-45.
9. De Stefano, C., Fontanella, F., Maniaci, M., Scotto Di Freca, A., Measuring layout features in mediaeval documents for writer identification (2019). IMEKO International Conference on Metrology for Archaeology and Cultural Heritage, MetroArchaeo 2017, pp. 356-359.
10. Cilia, N.D., De Stefano, C., Fontanella, F., Scotto di Freca, A., Variable-length representation for EC-based feature selection in high-dimensional data (2019). In: Kaufmann P., Castillo P. (eds) Applications of Evolutionary Computation. EvoApplications 2019. Lecture Notes in Computer Science, vol 11454. Springer, Cham, pp. 325-340.
11. Cilia, N.D., De Stefano, C., Fontanella, F., Scotto di Freca, A., Improving handwritten character recognition by using a ranking-based feature selection approach (2019). In: Vera-Rodriguez R., Fierrez J., Morales A. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2018. Lecture Notes in Computer Science, vol 11401. Springer, Cham, pp. 902-910.
12. Cilia, N.D., De Stefano, C., Fontanella, F., Molinara, M., Scotto Di Freca, A., Using handwriting features to characterize cognitive impairment (2019). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11752 LNCS, pp. 683-693.
13. Cilia, N.D., De Stefano, C., Fontanella, F., Molinara, M., Scotto di Freca, A., Minimizing Training Data for Reliable Writer Identification in Medieval Manuscripts (2019). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11808 LNCS, pp. 198-208.
14. Cilia, N.D., De Stefano, C., Fontanella, F., Molinara, M., Scotto Di Freca, A., Handwriting Analysis to Support Alzheimer’s Disease Diagnosis: A Preliminary Study (2019). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11679 LNCS, pp. 143-151.
15. Cilia, N.D., De Stefano, C., Fontanella, F., Raimondo, S., di Freca, A.S., An experimental comparison of feature-selection and classification methods for microarray datasets (2019) Information (Switzerland), 10 (3), 109.
16. Cilia, N.D., De Stefano, C., Fontanella, F., Marrocco, C., Molinara, M., Scotto di Freca, A., A Page-Based Reject Option for Writer Identification in Medieval Books (2019) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11808 LNCS, pp. 187-197.
17. Cilia, N.D., De Stefano, C., Fontanella, F., Marrocco, C., Molinara, M., Scotto Di Freca, A., A Two-Step System Based on Deep Transfer Learning for Writer Identification in Medieval Books (2019). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11679 LNCS, pp. 305-316.
2018
18. De Stefano, C., Maniaci, M., Fontanella, F., Scotto di Freca, A., Layout measures for writer identification in mediaeval documents (2018). Measurement: Journal of the International Measurement Confederation, 127, pp. 443-452.
19. De Stefano, C., Maniaci, M., Fontanella, F., Scotto di Freca, A., Reliable writer identification in medieval manuscripts through page layout features: The “Avila” Bible case (2018). Engineering Applications of Artificial Intelligence, 72, pp. 99-110.
20. Cilia, N.D., De Stefano, C., Fontanella, F., Di Freca, A.S., An experimental protocol to support cognitive impairment diagnosis by using handwriting analysis (2018). Procedia Computer Science, 141, pp. 466 471.
21. N.D. Cilia, C. De Stefano, F. Fontanella, and A. Scotto di Freca, Improving Evolutionary Algorithm Performance for Feature Selection in High-Dimensional Data (2018). In Applications of Evolutionary Computation, EvoApplications, 2018, Sim K., Kaufmann P. (eds), Lecture Notes in Computer Science, Springer, Cham, LNCS vol. 10784, pp. 439-454.
22. De Stefano, C., Fontanella, F., Marcelli, A., Parziale, A., Scotto di Freca, A., Recovering Segmentation Errors in Handwriting Recognition Systems (2018). In: Huang DS., Jo KH., Zhang XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science, vol 10955. Springer, Cham, pp. 631-642.
2017
23. Santoro, A., De Stefano, C., Marcelli, A., Assisted Transcription of Historical Documents by Keyword Spotting: A Performance Model (2017). Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 1, pp. 971-976.
24. C. De Stefano, F. Fontanella, and A. Scotto di Freca, “A Novel Mutation Operator for the Evolutionary Learning of Bayesian Networks”, in Proc. of the 2017 IEEE Congress on Evolutionary Computation (CEC 2017), San Sebastian, Spain, 5-8 June 2017, IEEE Computer Society, pp. 1999-2016, DOI: 10.1109/CEC.2017.7969546.
25. C. De Stefano, F. Fontanella, and A. Scotto di Freca, “Feature Selection in High Dimensional Data by a Filter-Based Genetic Algorithm”, in Applications of Evolutionary Computation. EvoApplications 2017, G. Squillero and K. Sim (eds), Lecture Notes in Computer Science, Springer, Cham, LNCS vol. 10199, pp. 506-521, DOI: https://doi.org/10.1007/978-3-319-55849-3_33.
26. C. De Stefano, F. Fontanella, D. Impedovo, G. Pirlo and A. Scotto di Freca, “A Brief Overview on Handwriting Analysis for Neurodegenerative Disease Diagnosys”, in Proc. of Workshop on Artificial Intelligence, D. Impedovo and G. Pirlo eds., CEUR Workshop Proceedings (CEUR-WS.org), Vol. 1982, Bari, Italy, November 14, 2017, pp. 9-16, URN: urn:nbn:de:0074-1982-0.
27. C. De Stefano, F. Fontanella, D. Impedovo, G. Pirlo and A. Scotto di Freca, “Handwriting analysis and e-health: a brief overview”, in Proc. of the 18th Conference of the International Graphonomics Society (IGS2017), Gaeta, Italy, 18-21 June 2017, pp. 143-147, ISBN: 9788864387062.
28. C. De Stefano, F. Fontanella and A. Scotto di Freca, “Feature Evaluation for Handwriting: a ranking-based approach”, in Proc. of the 18th Conference of the International Graphonomics Society (IGS2017), Gaeta, Italy, 18-21 June 2017, pp. 24 28, ISBN: 9788864387062.
2016
29. A. Gallozzi, G. Carbone, M. Ceccarelli, C. De Stefano, A. Scotto di Freca, M. Bianchi and M. Cigola, “The MuseBot project: Robotics, informatics, and economics strategies for museums”, in Handbook of Research on Emerging Technologies for Digital Preservation and Information Modeling, 12 September 2016, IGI Global, pp. 45-66, DOI: 10.4018/978-1-5225-0680-5.ch003.
30. C. De Stefano, F. Fontanella and A. Scotto di Freca, “A Novel GA-based Feature Selection Approach for High Dimensional Data”, in Proc. of the 2016 Genetic and Evolutionary Computation Conference Companion (GECCO 2016), Denver, United States, 20-24 July 2016, ACM, pp. 87-88, DOI: 10.1145/2908961.2909049.
2015
31. C. De Stefano, A. Marcelli and A. Parziale, “Quantitative evaluation of features for Forensic Handwriting Examination”, in Proc. of the International Conference on Document Analysis and Recognition (ICDAR), Nancy, France, 23-26 August 2015, IEEE Computer Society, pp. 1266 1271, DOI: 10.1109/ICDAR.2015.7333952.
32. C. De Stefano, F. Fontanella, A. Marcelli, A. Parziale and A. Scotto di Freca, “Feature evaluation for discriminating handwriting fragments”, in Proc. of the 17th International Conference of the Graphonomics Society - Drawing, Handwriting Processing Analysis: New Advances and Challenges, Guadeloupe, France, 21-25 June 2015, Martinique Universite des Antilles, pp. 29-32, ISBN:9791095177012
2014
33. C. De Stefano, G. Folino, F. Fontanella and A. Scotto di Freca, “Using Bayesian Networks for Selecting Classifiers in GP Ensembles”, Information Sciences, vol. 258, 2014, pp. 200-216, DOI: http://dx.doi.org/10.1016/j.ins.2013.09.049.
34. C. De Stefano, F. Fontanella, C. Marrocco and A. Scotto di Freca, “A GA-based Feature Selection Approach with an Application to Handwritten Character Recognition”, Pattern Recognition Letters, vol. 35(1), 2014, pp. 130–141, DOI: http://dx.doi.org/10.1016/j.patrec.2013.01.026.
35. C. De Stefano, F. Fontanella, A. Marcelli, A. Parziale and A. Scotto di Freca, “Rejecting both segmentation and classication errors in handwritten form processing”, in Proc. of the 14th International Conference on Frontiers in Handwriting Recognition (ICFHR 2014), Crete, Greece, 1-4 September, 2014, IEEE Computer Society, pp. 569-574, DOI: 10.1109/ICFHR.2014.101.
36. L.P. Cordella, C. De Stefano, F. Fontanella, A. Scotto di Freca, "Random Forest for Reliable Pre-Classification of Handwritten Characters", in Proc. of the 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden, 24-28 August, 2014, IEEE Computer Society, pp. 1319-1324, DOI: 10.1109/ICPR.2014.236.
2013
37. L.P. Cordella, C. De Stefano, F. Fontanella and A. Marcelli, "EvoGeneSys, a New Evolutionary Approach to Graph Generation”, Applied Soft Computing, Elsevier, vol. 13, issue 4, April 2013, pp. 1922–1938, DOI: https://doi.org/10.1016/j.asoc.2012.12.037.
38. C. De Stefano; A. Della Cioppa; A. Marcelli, “Evolutionary Approaches for Pooling Classifier Ensembles: Performance Evaluation”, in Proc. of the 5th International Conference of Soft Computing and Pattern Recognition (SoCPaR 2013), Hanoi, Vietnam, 15-18 December 2013, IEEE Computer Society, pp. 310-315, DOI: 10.1109/SOCPAR.2013.7054149.
39. L.P. Cordella, C. De Stefano, F. Fontanella, and A. Scotto di Freca, “A Weighted Majority Vote Strategy Using Bayesian Networks”, in Image Analysis and Processing – ICIAP 2013, A. Petrosino (eds.), Lecture Notes in Computer Science, vol 8157, Springer, Berlin, Heidelberg, pp. 219-228, DOI: https://doi.org/10.1007/978-3-642-41184-7_23.
40. C. De Stefano, F. Fontanella, M. Maniaci and A. Scotto di Freca, “Combining Single Class Classifiers for Mediaeval Handwriting Analysis: Individual Feature Selection as a Clue to Scribal Hand Distinction”, in Recent Progress in Graphonomics: Learn from the Past - Proc. of the 16th International Graphonomics Society Conference (IGS 2013), Nara, Japan, 11-13 June 2013, University of Tokyo Press, pp. 171-174, ISBN:9784904309094.
2012
41. C. De Stefano, F. Fontanella, G. Folino, A. Scotto di Freca, “Pruning GP–Based Classifier Ensembles by Bayesian Networks”, in Parallel Problem Solving from Nature, C. Coello et al. (eds.), Lecture Notes in Computer Sciences, vol. 7491, 2012, Springer-Verlag, Berlin Heidelberg, pp. 236-245, DOI: 10.1007/978-3-642-32937-1_24, ISBN: 978-3-642-32936-4.
42. C. De Stefano, F. Fontanella, and A. Scotto di Freca, “A Novel Naive Bayes Voting Strategy for Combining Classifiers”, Proc. of the 13th International Conference on Frontiers in Handwriting Recognition (ICFHR 2012), September 18-20, 2012, Bari, Italy, IEEE Computer Society, pp. 465-470, DOI: 10.1109/ICFHR.2012.166.
2011
43. C. De Stefano, A. Marcelli, Marco Rendina, “Disguising Writers Identification: an Experimental Study”, Journal of Forensic Document Examination, vol. 21, 2011, pp. 23-35, ISBN: 0895-0849.
44. C. De Stefano, F. Fontanella, G. Folino and A. Scotto di Freca, “A Bayesian Approach for Combining Ensembles of GP Classifiers”, in Multiple Classifier Systems - MCS 2011, C. Sansone et al. (Eds), Lecture Notes in Computer Science, vol. 6713, 2011, Springer, Berlin, Heidelberg, pp. 26-35, DOI: 10.1007/978-3-642-21557-5_5.
45. C. De Stefano, F. Fontanella, M. Maniaci, A. Scotto di Freca, “A Method for Scribe Distinction in Medieval Manuscripts Using Page Layout Features”, in Image analysis and processing - ICIAP 2011, G. Maino and G.L. Foresti (Eds.), Lecture Notes in Computer Science, vol. 6978, 2011, Springer, Berlin, Heidelberg, pp. 393-402, DOI: https://doi.org/10.1007/978-3-642-24085-0_41.
46. C. De Stefano, G. Folino, F. Fontanella and A. Scotto di Freca, “Using Bayesian Networks for Selecting Classifiers in GP Ensembles”, in Proc. of the 13th Genetic and Evolutionary Computation Conference (GECCO 2011), Dublin, Ireland, 12-16 July, 2011, ACM, pp. 173-174, DOI: 10.1145/2001858.2001955.
47. C. De Stefano, F. Fontanella , M. Maniaci and A. Scotto di Freca, “Exploiting Page Layout Features for Scribe Distinction in Medieval Manuscripts”, in Proc. of the 15th International Graphonomics Society Conference (IGS 2011), Cancun, MEXICO, 12 15 June, 2011, IGS press, pp. 106-109.
48. C. De Stefano, A. Marcelli and A. Parziale, “Segmenting Isolated Characters Within Cursive Words”, in Proc. of the 15th International Graphonomics Society Conference (IGS 2011), Cancun, MEXICO, 12 15 June, 2011, IGS press, pp. 156-159.
2010
49. C. De Stefano, F. Fontanella, C. Marrocco and A. Scotto di Freca, “A Hybrid Evolutionary Algorithm for Bayesian Networks Learning: an Application to Classifier Combination”, in Lecture Notes in Computer Science: Applications of Evolutionary Computation, C. Di Chio et al. (Eds.), Springer Berlin / Heidelberg, vol. 6024, 2010, DOI: 10.1007/978-3-642-12239-2_23, ISBN: 978-3-642-12238-5, pp. 221-230.
50. L.P. Cordella, C. De Stefano, F. Fontanella, C. Marrocco, A. Scotto di Freca, “Combining Single Class Features for Improving Performance of a Two Stage Classifier”, in Proc. of the 20th International Conference on Pattern Recognition (ICPR 2010), 23-26 August 2010, Istanbul, Turkey, IEEE Computer Society, Los Alamitos, CA; USA 90720-1314, DOI: 10.1109/ICPR.2010.1058, ISBN: 978-0-7695-4109-9, pp. 4352-4355.
51. L.P. Cordella, C. De Stefano, A. Marcelli, A. Santoro, “Writing Order Recovery from off-line Handwriting by Graph Traversal”, in Proc. of the 20th International Conference on Pattern Recognition, 23-26 August 2010, Istanbul, Turkey, IEEE Computer Society, Los Alamitos, CA; USA 90720-1314, DOI: 10.1109/ICPR.2010.467, ISBN: 978-0-7695-4109-9, pp. 1896-1899.
52. C. De Stefano, A. Marcelli, A. Parziale, R. Senatore, “Reading Cursive Handwriting”, in Proc. of the 12th International Conference on Frontiers in Handwriting Recognition, 16-18 November 2010, Calcutta, India, IEEE Computer Society, Los Alamitos, CA; USA 90720-1314, pp.95-100, ISBN: 978-0-7695-4221-8, Print ISBN: 978-1-4244-8353-2, DOI: 10.1109/ICFHR.2010.21.
2009
53. C. De Stefano, C. D'Elia, A. Marcelli and A. Scotto di Freca, “Classifier Combination by Bayesian Networks for Handwriting Recognition”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 23, No. 5 (2009), pp. 887-905.
54. C. De Stefano, F. Fontanella, A. Marcelli, A. Scotto di Freca, “Learning Bayesian Networks by Evolution for Classifier Combination”, in Proc. of the 10th International Conference on Document Analysis and Recognition ICDAR 2009, Barcelona, Spain, July 26-29, 2009, pp. 966-970.
55. C. De Stefano, F. Fontanella, A. Marcelli, A. Scotto di Freca, “Using Bayesian networks for combining classifiers: a new evolutionary learning algorithm”, in Proc. of the 14th Conference of the International Graphonomics Society, Dijon, France, September 13 16, 2009, pp. 162-165.
56. C. De Stefano, A. Marcelli and M. Rendina, “Disguising writers identification: an experimental study”, in Proc. of the 14th Conference of the International Graphonomics Society, Dijon, France, September 13 16, 2009, pp. 99-102.
2008
57. C. De Stefano, F. Fontanella, and C. Marrocco, “A GA-Based Feature Selection Algorithm for Remote Sensing Images”, in Lecture Notes in Computer Sciences, M. Giacobini et al. (Eds.), Springer-Verlag Berlin Heidelberg, vol. 4974, 2008, pp 285–294.
58. C. De Stefano, A. Della Cioppa and A. Marcelli, “Dynamic selection of classifiers: when few are better than all”, Proc. of the 11th International Conference on Frontiers in Handwriting Recognition 2008 (ICFHR 2008), August 19-21, 2008, Montreal, Quebec, Canada, pp.433-438.
59. L.P. Cordella, C. De Stefano, F. Fontanella and C. Marrocco, A Feature Selection Algorithm for Handwritten Character Recognition”, Proc. of the 19th International Conference on Pattern Recognition (ICPR 2008), December, 8-11, 2008, Tampa, Florida, USA, pp. 1-4.
60. A. Clavelli, L.P. Cordella, C. De Stefano and A. Marcelli, “Indexing and Retrieving Cursive Documents without Recognition” Proc. of the 19th International Conference on Pattern Recognition (ICPR 2008), December, 8-11, 2008, Tampa, Florida, USA, pp. 1-4.
2007
61. C. De Stefano, A. Della Cioppa, A. Marcelli, “Where are the niches? The Dynamic Fitness Sharing”, IEEE Transactions on Evolutionary Computation, vol. 11(4), August 2007, pp. 453-465.
62. C. De Stefano, C. D’Elia, A. Marcelli and A. Scotto di Freca, “Incorporating a wavelet transform into a saliency-based method for on-line handwriting segmentation”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 21(1), February 2007, pp. 43-59.
63. L.P. Cordella, C. De Stefano, F. Fontanella, “Evolutionary Prototyping for Handwriting Recognition”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 21(1), February 2007, pp. 157-178.
64. C. De Stefano, A. Marcelli, “Advances in Graphonomics for Handwriting Analysis and Recognition”, International Journal on Pattern Recognition and Artificial Intelligence, vol. 21(1), February 2007, pp. 1 4.
65. C. De Stefano, F. Fontanella, C. Marrocco and G. Schirinzi, “A Feature Selection Algorithm for Class Discrimination Improvement”, Proc. of the IEEE International Geoscience and Remote Sensing Symposium – IGARSS 07, Barcelona, Spain, 23-27 July 2007, pp. 425-428.
66. C. De Stefano, C. D’Elia, A. Marcelli, A. Scotto di Freca, “Using Bayesian Network for Combining Classifiers”, Proc. of the Int. Conf. on Image Analysis and Processing – ICIAP’07, Modena (ITALY), September 10-14, 2007, pp.73-78.
67. C. De Stefano, C. D’elia, A, Marcelli and A. Scotto Di Freca, “Bayesian Networks for Multi-expert Character Classification System”, Proc. of the 13th International Conference of the International Graphonomics Society, Melbourne, Australia, November 11-14, 2007, pp. 48-51.
68. A. Clavelli, C. De Stefano and A Marcelli, “Handwriting Generation for Writing Order Recovery”, Proc. of the 13th International Conference of the International Graphonomics Society, Melbourne, Australia, November 11-14, 2007, pp. 32-35.
69. C. De Stefano, A. Marcelli, and A. Santoro, “On-line Cursive Recognition by Ink Matching”, Proc. of the 13th International Conference of the International Graphonomics Society, Melbourne, Australia, November 11-14, 2007, pp. 23-27.
2006
70. L.P. Cordella, C. De Stefano, F. Fontanella, A. Marcelli, “Looking for Prototypes by Genetic Programming”, in Lecture Notes in Computer Sciences, N. Zheng, X. Jiang, and X. Lan eds., Springer-Verlag, vol. 4153, 2006, pp. 152-159.
71. L.P. Cordella, C. De Stefano, F. Fontanella, A. Marcelli, “Evolutionary Generation of Prototypes for a Learning Vector Quantization Classifier”, in Lecture Notes in Computer Sciences, F. Rothlauf et al. eds., Springer-Verlag, vol. 3907, 2006, pp. 391-402
72. C. De Stefano, A. Della Cioppa, A. Marcelli, “An Evolutionary Approach for Dynamic Configuration of Multi-expert Classification Systems “ Proc. of the IEEE World Congress on Computational Intelligence, Vancouver BC, Canada, July 16-21, 2006, pp. 8613-8619.
73. C. De Stefano, C. D’Elia, A. Marcelli and A. Scotto di Freca, “Improving Dynamic Learning Vector Quantization”, Proc. of the 18th International Conference on Pattern Recognition 2006 (ICPR 2006), Hong Kong (PRC), 20-24 Aug. 2006, Volume 2, Page(s):804 – 807.
2005
74. L.P. Cordella, C. De Stefano, F. Fontanella, A. Marcelli, “EvoGeneS, a New Evolutionary Approach to Graph Generation”, in Lecture Notes in Computer Science, Evolutionary Computation in Combinatorial Optimization, Springer-Verlag, vol. 3448, 2005, pp. 46-57.
75. C. De Stefano, M. Garruto, L. Lapresa, A. Marcelli, “Using Strings for On-Line Handwriting Shape Matching: a New Weighted Edit Distance”, in Lecture Notes in Computer Science, F. Roli and S. Vitulano eds., Springer-Verlag, vol. 3617, 2005, pp 1125-1132.
76. L. P. Cordella, C. De Stefano, F. Fontanella, A. Marcelli, “A Novel Genetic Programming Based Approach for Classification Problems”, in Lecture Notes in Computer Science, F. Roli and S. Vitulano eds., Springer-Verlag, vol. 3617, 2005, pp. 727-734.
77. L. P. Cordella, C. De Stefano, F. Fontanella, A. Marcelli, “Genetic Programming for Generating Prototypes in Classification Problems”, Proc. of the 2005 IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, UK, Sepember 2-5, 2005, vol. 2, pp. 1149-1155.
78. L. P. Cordella, C. De Stefano, F. Fontanella, A. Marcelli, “An Evolutionary Clustering Method for Handwriting Recognition”, in: Advances in Graphonomics, A. Marcelli and C. De Stefano (eds.), Arezzo: Zona Editrice, ISBN-13:9788889702130, June 2005, pp. 103 107.
79. L. Avallone, C. De Stefano, C. Gambone, A. Marcelli, “Visual Processes and Features in Human Reading of Handwriting”, in: Advances in Graphonomics, A. Marcelli and C. De Stefano (eds.), Arezzo: Zona Editrice, June 2005, pp. 128-132.
80. C. De Stefano, C. D’Elia, M. Garruto, A. Marcelli, A. Scotto di Freca, “A Wavelet Based Curve Decomposition for On-line Handwriting”, in: Advances in Graphonomics, A. Marcelli and C. De Stefano (eds.), Arezzo: Zona Editrice, June 2005, pp. 162-167.
81. C. De Stefano, M. Garruto, L. Lapresa, A. Marcelli, “Detecting Handwriting Primitives in Cursive Words by Stroke Sequence Matching”, in: Advances in Graphonomics, A. Marcelli and C. De Stefano (eds.), Arezzo: Zona Editrice, June 2005, pp. 281-285.
2004
82. C. De Stefano, A. Della Cioppa, A. Marcelli, “On the Role of Population Size and Niche Radius in Fitness Sharing”, IEEE Transactions on Evolutionary Computation, vol. 8, no.6, 2004, pp. 580-592.
83. C. De Stefano, G. Guadagno, A. Marcelli, “A saliency-based segmentation method for on-line cursive handwriting”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 18, no. 7, 2004, pp. 1139-1156.
84. C. De Stefano, A. Marcelli, “An Efficient Method for On-Line Cursive Handwriting Strokes Reordering”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 18, no.7, 2004, pp. 1157-1171.
85. C. De Stefano, M. Garruto, and A. Marcelli, “A saliency-based multiscale method for on-line cursive handwriting shape description”, Proc. of the 9th International Workshop on Frontiers in Handwriting Recognition 2004 (IWFHR-9), Tokyo, Japan, October, 26-29, 2004, pp. 124-129.
86. C. De Stefano, C. D’Elia, A. Marcelli, “A Dynamic Approach to Learning Vector Quantization”, Proc. of the 17th International Conference on Pattern Recognition 2004 (ICPR 2004), Cambridge, UK, August 23-26, vol. 4, 2004, pp. 601-604.
87. C. De Stefano, M. Garruto, A. Marcelli, “A Multiresolution Approach to On-line Handwriting Segmentation and Feature Extraction”, Proc. of the 17th International Conference on Pattern Recognition 2004 (ICPR 2004), Cambridge, UK, August 23-26, vol. 2, 2004, pp. 614-617.
2003
88. C. De Stefano, A. Della Cioppa, A. Marcelli, “Exploiting Reliability for Dynamic Selection of Classifiers by Means of Genetic Algorithms”, Proc. of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003), Edinburgh, Scotland, August 3-6, 2003, pp. 671-675.
89. C. De Stefano, G. Guadagno, A. Marcelli, “A decomposition method for on-line cursive handwriting based on multi-scale representation”, Proc of the 11th Biennial Conference of The International Graphonomics Society (IGS 2003), Scottsdale, Arizona, USA, November 2 5, 2003, pp. 212-215.
90. G. D’Andria, C. De Stefano, R. Foglia, A. Marcelli “An algorithm for handwriting strokes reordering”, Proc of the 11th Biennial Conference of The International Graphonomics Society (IGS 2003), Scottsdale, Arizona, USA, November 2 5, 2003, pp. 237-240.
2002
91. C. De Stefano, A. Della Cioppa and A. Marcelli, “Character Preclassification based on Genetic Programming”, Pattern Recognition Letters, vol. 23, no. 12, 2002, pp.1439-1448.
92. C. De Stefano, A. Della Cioppa, A Marcelli, “Learning Handwriting by Evolution: A Conceptual Framework for Performance Evaluation and Tuning”, Pattern Recognition, vol. 35, no .5, 2002, pp. 1025-1037.
93. C. De Stefano, A. Della Cioppa and A Marcelli, “Adaptation And Learning For Pattern Recognition: A Comparison Between Neural And Evolutionary Computation”, in Human and Machine Perception 3 – Thinking, Deciding, Acting, V. Cantoni et al (eds), Kluwer Academic/Plenum Publishers, January 2002, pp. 149-157.
94. C. De Stefano, A. Della Cioppa, A. Marcelli, “An adaptive weighted majority rule for combining multiple classifiers”, Proc of the International Conference on Pattern Recognition 2002 (ICPR 2002), Quebec City, (CANADA), August 13-16, 2002, vol. 2, pp. 192-195.
95. C. De Stefano, A. Marcelli, “From ligatures to characters: a shape-based algorithm for handwriting segmentation”, Proc of the International Workshop on Frontiers of Handwriting Recognition 2002 (IWFHR 2002), Niagara-on-the-lake, ON (CANADA), August 6-8, 2002, pp. 473-478.
2001
96. C. De Stefano, A. Iuliano, A. Marcelli, “A Shape-Based Algorithm for Detecting Ligatures in On-line Handwriting”, Intelligent Automation and Soft Computing – An International Journal, vol. 7, no. 3, 2001, pp. 187-194. doi = {10.1080/10798587.2000.10642816}.
97. C. De Stefano and M. Frucci, “Spatial Relations among Pattern Subsets as a guide for Skeleton Pruning”, in Lecture Notes in Computer Science, C. Arcelli et al. eds., Springer-Verlag, vol. 2059, 2001, pp. 155-164.
98. C. De Stefano, A. Della Cioppa and A. Marcelli, “ Grouping Character Shapes by means of Genetic Programming”, in Lecture Notes in Computer Science, C. Arcelli et al. eds., Springer-Verlag, vol. 2059, 2001, pp. 504-513.
99. C. De Stefano, L. Di Lieto, .A Marcelli, “Detecting ascenders and descenders in cursive handwriting”, Proc of the 10th Biennial Conference of The International Graphonomics Society (IGS 2001), University of Nijmegen, The Netherlands, August 6-8, 2001, pp. 154 159.
100. C. De Stefano, A. Della Cioppa and A. Marcelli, “An Investigation on MPEG Audio Segmentation by Evolutionary Algorithms”, Proc. of the Sixth International Conference on Document Analysis and Recognition (ICDAR 2001), Seattle, Washington, U.S.A., September 10-13, 2001, pp. 952-956.
2000
101. C. De Stefano, C. Sansone, M. Vento “To Reject or not To Reject: That’s The Question… An Answer in Case of Neural Classifiers”, IEEE Transactions on Systems, Man and Cybernetics, vol. 30, Part C, No. 1, February 2000, pp. 84-94.
102. L.P. Cordella, C. De Stefano and M. Frucci, “A Robust Shape Decomposition Method”, in Lecture Notes in Computer Science, Atul K. Chhabra and Dov Dori eds., Springer-Verlag, vol. 1941, 2000, pp. 219 227.
1999
103. C. De Stefano, A. Della Cioppa and A. Marcelli, “Modeling the Tradeoff between Completeness and Consistency in Genetics-Based Handwritten Character Prototyping”, in: Document Recognition and Retrieval VI - Proceedings of SPIE - D.P. Lopresti and J. Zhou (eds.), vol. 3651, San Jose, California, January 27-28, 1999, pp. 64-72.
104. C. De Stefano, A. Della Cioppa, A. Marcelli, “Evolutionary Learning for Handwriting Recognition: Performance Evaluation”, Proc. of The 9th International Graphonomics Society Conference (IGS 99), Singapore, June 28-30, 1999, pp. 145-150.
105. C. De Stefano, A. Della Cioppa, .A Marcelli, “Handwritten Numeral Recognition by means of Evolutionary Algorithms”, Proc. of the Fifth International Conference on Document Analysis and Recognition (ICDAR ’99), Bangalore, India, September 20 22, 1999, pp. 804-807.
106. L.P. Cordella, C. De Stefano, M. Frucci, “Hierarchical Shape Decomposition for Non Elongated Figure Description”, Proc. of The Third IAPR International Workshop on Graphics Recognition (GREC 99), Jaipur, India, September 26-27, 1999, pp. 211-218.
107. L.P. Cordella, C. De Stefano, A. Della Cioppa, .A Marcelli, “A New Evolutionary Learning Model for Handwritten Character Prototyping”, Proc. of the 10th International Conference on Image Analysis and Processing (ICIAP ’99), Venice, Italy, September 27-29, 1999, pp. 830 835.
1998
108. L.P. Cordella, C. De Stefano, C. Sansone, F. Tortorella, M. Vento, “Neural Networks Classification Reliability: Problems and Applications”, in Neural network Systems Techniques and Applications, International Series on Advances in Control and Dynamic Systems, vol. 5: Image Processing and Pattern Recognition, C.T. Leondes Ed., Academic Press, 1998, ISBN-13: 9780124438651, ISBN: 0124438652, pp. 161 199.
109. C. De Stefano, A. Della Cioppa, A. Marcelli, “Exploiting Genetic Algorithms with Niching for Pattern Recognition”, Proc. of The Fifth International Conference on Control, Automation, Robotics and Vision (ICARCV ’98), Singapore, December 9-11, 1998, vol. 2, pp. 1382-1386.
1997
110. C. De Stefano, A. Iuliano, A. Marcelli, “Towards a Hierarchical Segmentation of On-line Cursive Script”, in Progress in Handwriting Recognition, A.C. Downton, S. Impedovo eds., World Scientific Publishing, Singapore, ISBN-10: 9810230842, ISBN-13: 978-9810230845, 1997, pp. 519-524.
111. C. De Stefano and A. Marcelli, "Generalization vs. Specialization: Quantitative Evaluation Criteria for Genetics-Based Learning Systems", Proc. of the 1997 IEEE International Conference on Systems Man and Cybernetics, Orlando Florida, USA, October 12-15, 1997, pp. 2865-2869.
112. C. De Stefano and A. Marcelli, "A shape based algorithm for segmenting on-line handwriting”, Proc. of the 8th Biennal Conference of the International Graphonomics Society (IGS ’97), Genova, Italy, August 24-28, 1997, pp. 85-86.
1996
113. C. De Stefano, P. Foggia, F. Tortorella and M. Vento, “A Distance Measure for Structural Descriptions Using Circle Arcs as Primitives”, Proc. of the 13th International Conference on Pattern Recognition (ICPR ‘96), Vienna, Austria, August 25 29, 1996, pp. 290-294.
1995
114. C. De Stefano, F. Tortorella and M. Vento, "An Entropy Based Method to Extract Robust Binary Templates", Machine Vision and Applications, Vol. 8, No. 3, 1995, pp. 173-178.
115. L.P. Cordella, C. De Stefano and M. Vento, "A Neural Network Classifier for OCR Using Structural Descriptions", Machine Vision and Applications, Vol. 8, No. 5, 1995, pp. 336-342.
116. L. P. Cordella, C. De Stefano, F. Tortorella and M. Vento, "A Method for Improving Classification Reliability of Multi-layer Perceptrons", IEEE Transactions on Neural Networks, Vol. 6, No. 5, September 1995, pp. 1140-1147.
117. L.P. Cordella, C. De Stefano, C. Sansone, M. Vento, “An Adaptive Reject Option for LVQ Classifiers”, in Lecture Notes in Computer Science - Image Analysis and Processing, C. Braccini et al. eds., Springer-Verlag, vol. 974, 1995, pp. 68-73.
118. L.P. Cordella, A. d’Acierno, C. De Stefano, M. Vento, “Mapping Schemes for Sequential Image Processing Algorihms”, in Computer Architecture for Machine Perception, V. Cantoni et al. eds., IEEE Comp. Soc. Press, 1995, pp. 184-189.
119. C. De Stefano, C. Sansone, M. Vento, "Comparing Generalization and Recognition Capability of Learning Vector Quantization and Multi-layer Perceptron Architectures”, Proc. of 9th Scandinavian Conference on Image Analysis (9SCIA), Uppsala, Sweden, 1995, pp. 1123 1130.
1994
120. L. P. Cordella, C. De Stefano, F. Tortorella and M. Vento, "Feature Selection for Optical Character Description", in Progress in Image Analysis an Processing III, S. Impedovo ed., World Scientific Publ. Singapore, 1994, pp. 373 376.
121. C. De Stefano, C. Sansone and M. Vento, “Evaluating Competitive Learning Strategies for Handwritten Character Recognition”, Proc. of 1994 IEEE International Conference on Systems, Man and Cybernetics (Humans, Information and Technology SMC), San Antonio, USA, October 2-5, 1994, pp. 759-764.
122. A. d’Acierno, C. De Stefano, F. Tortorella and M. Vento, “Can a Sequential Thinning Algorithm Be Parallelized ?”, Proc. of The 12th International Conference on Pattern Recognition, Jerusalem, Israel, October 9-13, 1994, pp. 360-362.
1993
123. L. P. Cordella, C. De Stefano, F. Tortorella and M. Vento, "Classification Rules for Supervised Neural Classifiers", Proc. of the 8-th Scandinavian Conference on Image Analysis, Tromso, 1993, pp. 539-545.
124. C. De Stefano, F. Tortorella and M. Vento, "Using Entropy for Drawing Reliable Templates", Proc. of the II International Conference on Document Analysis and Recognition, Tokyo, 1993, pp. 345-348.
1992
125. C. De Stefano, F. Tortorella and M. Vento, "A Method for the Recognition of Symbols on Geographic Maps", Proc. of the 11-th International Conference on Pattern Recognition, The Hague, 1992, vol. I, pp. A734-737.
126. L.P. Cordella, C. De Stefano, F. Tortorella and M. Vento, "Improving Character Recognition Rate by a Multi Net Neural Classifier", Proc. of the 11-th International Conference on Pattern Recognition, The Hague, 1992, vol. II, pp. B715 718.
127. C. De Stefano, F. Tortorella and M. Vento, "Morphological Functions for Symbol Recognition on Geographic Maps", Proc. of the 2nd International Conference on Automation Robotics and Computer Vision, Singapore, 1992, pp. CV 21.3.1 CV 21.3.5.
1991
128. A. d' Acierno, C. De Stefano and M. Vento, "A Multi-Net Neural Classifier Tailored by means of Test Set Characterization" in Parallel Architecture and Networks, E.R. Caianiello ed., World Scientific Publ. Singapore, 1991, pp. 321 325.
129. L.P. Cordella, A. Chianese, M. De Santo, C. De Stefano and M. Vento, "On the Comparison Among Description Methods by Neural Networks", in Progress in Image Analysis and Processing II, Cantoni et al. eds., World Scientific Publ. Singapore, 1991, pp. 461-465.
130. A. d'Acierno, C. De Stefano and M. Vento, "A Neural Classifier for Structural Character Recognition", Proc. of the 7-th Scandinavian Conference on Image Analysis, Aalborg, 1991, pp. 886-893.
131. A. d'Acierno, C. De Stefano and M. Vento, "A Structural Character Recognition Method Using Neural Networks", Proc. of the First International Conference on Document Analisys and Recognition, Saint Malo', ISBN: 2-903677100-2, 1991, pp. 803-811.

[Ultima modifica: mercoledì 30 novembre 2016]