[{"authors":null,"categories":null,"content":"Biological and biomedical data and image analysis  The Group was involved in research to design of machine learning and soft computing based techniques for biological and biomedical data and image analysis: \\\n  analysis of brain and skeletal NMR and TAC images. The careful delineation of the medical objects alone provides relevant clinical information and the extraction of quantitative features is fundamental, once the diagnosis was made, to determine the extent and progression of the disease. The activities concern the development of techniques based on artificial vision and fuzzy pattern recognition of shapes to detect anomalies in magnetic resonance and tomographic images.\n  analysis of cardio-vascular eco images. The cardiovascular and cerebrovascular diseases are the major causes of mortality in the population and the complex inner-media (IMT) of the carotid artery can be used to predict cardiovascular events such as myocardial infarction. Since the manual analysis is tiring, does not guarantee reproducibility and does not allow the analysis of other important statistics, activity concerns the development of a CAD (Computer Aided Diagnosis) based on deformable models to automatically extract the characteristics of the intimate media structure from images of the carotid artery.\n  analysis of images in functional genomics. The data of functional genomics, typically images and sequences of images, tend to identify the mechanisms that regulate the activation or deactivation of genes in various experimental conditions, such as outbreaks of diseases or sequences of observations in therapeutic phases. The activities concern the development of CAD based on Bayesian techniques to extract the activation parameters of genes and proteins, addressing issues such as gridding, the segmentation of microarray images, image registration in proteomics and image analysis in biology cellular.\n  \\\n","date":1542247370,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1542247370,"objectID":"8e6af190396a285b915a865ee61e830c","permalink":"/research/biological/","publishdate":"2018-11-14T19:02:50-07:00","relpermalink":"/research/biological/","section":"research","summary":"Biological and biomedical data and image analysis  The Group was involved in research to design of machine learning and soft computing based techniques for biological and biomedical data and image analysis: \\\n  analysis of brain and skeletal NMR and TAC images. The careful delineation of the medical objects alone provides relevant clinical information and the extraction of quantitative features is fundamental, once the diagnosis was made, to determine the extent and progression of the disease.","tags":null,"title":"Biological and biomedical data and image analysis","type":"research"},{"authors":null,"categories":null,"content":"Intelligent Processing of Spatio-temporal signals    Clustering, that is discovery of groups of \u0026ldquo;similar\u0026rdquo; trajectories. As an example, the cluster of trajectories they can bring to light the presence of paths not adequately covered from the public transit service.\n  Frequent pattern, that is the discovery of frequent paths. These information could be useful for the city planning, as an example, evidencing frequently covered paths followed by vehicles, that could be the result of planning of the devoid traffic.\n  Classification, that is the discovery of behaviour rules, aiming to explain the behaviour of the running customers and to foretell that one of the future customers. An application could be the pre-allocation of resources.\n  \nFrom the methodological standpoint, the research activity investigates machine learning approaches and specifically neuro-fuzzy models. \\\n","date":1542247370,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1542247370,"objectID":"6eb54fc22d11544542af51ebd69b4be5","permalink":"/research/intelligent/","publishdate":"2018-11-14T19:02:50-07:00","relpermalink":"/research/intelligent/","section":"research","summary":"Intelligent Processing of Spatio-temporal signals    Clustering, that is discovery of groups of \u0026ldquo;similar\u0026rdquo; trajectories. As an example, the cluster of trajectories they can bring to light the presence of paths not adequately covered from the public transit service.\n  Frequent pattern, that is the discovery of frequent paths. These information could be useful for the city planning, as an example, evidencing frequently covered paths followed by vehicles, that could be the result of planning of the devoid traffic.","tags":null,"title":"Intelligent Processing of Spatio-temporal signals","type":"research"},{"authors":null,"categories":null,"content":"Soft Computing In Image Analysis  The rise of several major seminal theories proposed in early 60’s including fuzzy logic, genetic algorithms, evolutionary computation, neural networks and their combination (the soft-computing paradigm in brief) allows to incorporate imprecision and incomplete information, and to model very complex systems, making them a useful tool in many scientific areas. These new methods may become more effective and powerful in real-world applications and can offer viable and effective solutions to some of the most difficult problems in image and pattern analysis. The research activity concerns the design of a computational model that takes advantage of the notion of rough fuzzy sets and learning to realize a system capable to efficiently cluster data coming from computer vision tasks. The hybrid notion of rough fuzzy sets comes from the combination of two models of uncertainty like vagueness by handling rough sets (Pawlak, 1985) and coarseness by handling fuzzy sets (Zadeh, 1975). Rough sets embody the idea of indiscernibility between objects in a set, while fuzzy sets model the ill-definition of the boundary of a sub-class of this set. Marrying both notions lead to consider, as instance, approximation of sets by means of similarity relations or fuzzy partitions. The proposed multiscale mechanism, based on a model of rough fuzzy sets is adopted to spread out local into more global information. The local features extracted by the consecutive layers are combined in the output layer in order to cluster the output neurons by minimizing the fuzziness of the output layer. This consitutes a fast algorithm for computing scale spaces, and apply them to image processing. We report results for region-based image segmentation and edge detection by minimizing measures of fuzziness, while texture segmentation is realized by optimizing parabolic-evolutive partial differential equations with edge preserving smoothing properties. An efficient block coding scheme is also designed upon the rough-fuzzy model, together with the adoption of machine learning techniques for vector quantization, as compared against Fuzzy Transform and Fuzzy Relational techniques.\nThe rough-fuzzy synergy is also adopted to better represent the uncertainty in colour image representation and histogram based indexing mechanisms. \\\n","date":1542247370,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1542247370,"objectID":"0427c46e190651e97ffc421b558485c4","permalink":"/research/soft/","publishdate":"2018-11-14T19:02:50-07:00","relpermalink":"/research/soft/","section":"research","summary":"Soft Computing In Image Analysis  The rise of several major seminal theories proposed in early 60’s including fuzzy logic, genetic algorithms, evolutionary computation, neural networks and their combination (the soft-computing paradigm in brief) allows to incorporate imprecision and incomplete information, and to model very complex systems, making them a useful tool in many scientific areas. These new methods may become more effective and powerful in real-world applications and can offer viable and effective solutions to some of the most difficult problems in image and pattern analysis.","tags":null,"title":"Soft Computing In Image Analysis","type":"research"},{"authors":null,"categories":null,"content":"Structured Pattern Recognition            In machine learning, very powerful and efficient methods have been proposed when data are represented by flat and fixed-width real vectors, even when heavily corrupted by noise. Neural networks, support vector machines and statistical methods are well known and widely used techniques. All of them share many successful stories in real-life problems, a well established theoretical background, and many journals and conferences devoted to explore possible refinements and applications. Unfortunately, in many relevant applications, data are not naturally expressed in terms of flat vectors. More expressive data structures, as trees or graphs, often nicely capture essential properties of the problem at hand, simplifying its mathematical representation and paving the way for its solution. Also, the features characterizing the input vectors are quantitative, i.e. numerical in nature, but features having imprecise or incomplete specification are usually either ignored or discarded from the design and test sets. The concept of Zadeh's fuzzy set theory can be introduced into the machine learning process to cope with impreciseness arising from various sources. For example, it may become convenient to use linguistic variables and hedges (small, medium, high, very, more and less, etc.) in order to describe the feature information. Again, uncertainty in classification may arise from the overlapping nature of classes; realistically speaking, the feature vector characterizing a specific pattern can and should be allowed to have degrees of membership in more than one class. The research activity concerns the design of neuro-fuzzy and kernels models for processing structured data. The studies relating the insertion of fuzzy rule-based domain knowledge and hence the fuzzy automaton state transitions into neural or kernel models should provide two benefits: (i) improving generalization to new instances and (ii) simplifying learning. The applications include 2D e 3D object recognition.\\   \\     ","date":1542247370,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1542247370,"objectID":"9e9c9148d903e475d507fe1c1052d99c","permalink":"/research/structured/","publishdate":"2018-11-14T19:02:50-07:00","relpermalink":"/research/structured/","section":"research","summary":"Structured Pattern Recognition            In machine learning, very powerful and efficient methods have been proposed when data are represented by flat and fixed-width real vectors, even when heavily corrupted by noise. Neural networks, support vector machines and statistical methods are well known and widely used techniques. All of them share many successful stories in real-life problems, a well established theoretical background, and many journals and conferences devoted to explore possible refinements and applications.","tags":null,"title":"Structured Pattern Recognition","type":"research"},{"authors":null,"categories":null,"content":"Tutorial on Fuzzy Systems and Networks: Theory and Applications  The tutorial focuses on some theoretical concepts for fuzzy systems and networks in the context of model complexity attributes such as nonlinearity, dimensionality and structure. In particular, rule base reduction and compression methods for fuzzy systems are considered. Also, fuzzy networks with chained and modular rule bases are discussed. The theoretical results are applied to several case studies and validated comparatively using established metrics for model performance indicators such as accuracy, efficiency and transparency.\nDates\nWednesday 27th June 2018: 10:00 am - 1:00 pm (Aula 2)\nThursday 28th June 2018: 10:00 am - 01:00 pm (Aula 2)\\\nLecturer\nAlexander Gegov from University of Portsmouth.\nShort bio Alexander Gegov is Reader in Computational Intelligence in the School of Computing, University of Portsmouth, UK. He holds a PhD in Control Systems and a DSc in Intelligent Systems – both from the BulgarianAcademy of Sciences. He has been Humboldt Guest Researcher at the University of Duisburg in Germany. He has also been EU Visiting Researcher at the University of Wuppertal in Germany and the Delft University of Technology in the Netherlands. Alexander Gegov’s research interests are in the development of computational intelligence methods and their application for modelling and simulation of complex systems and networks. He has edited a few books and authored several research monographs plus a dozen of book chapters - most of them published by Springer. He has published a significant number of articles and papers in international journals and conferences including ones managed and organised by IEEE. He has presented invited lectures and tutorials at a wide range of international research events including IEEE Conferences and Summer Schools on Fuzzy Systems, Intelligent Systems, Computational Intelligence and Cybernetics.\nMain Aims\n  Theoretical knowledge in fuzzy logic.\n  Applied knowledge in fuzzy systems and networks.\n  Learning Outcomes\n  Explain and interpret the mathematical foundations of fuzzy logic.\n  Use and apply techniques for building fuzzy systems and networks.\n  Syllabus Outline\n  Sets and relations for fuzzy rule based systems.\n  Fuzzification, inference and defuzzification in fuzzy rule based systems.\n  Mamdani, Sugeno and Tsukamoto fuzzy rule based systems.\n  Modelling, simulation and control of fuzzy rule based systems.\n  Formal models for fuzzy rule based networks.\n  Basic and advanced operations in fuzzy rule based networks.\n  Feedforward and feedback fuzzy rule based networks.\n  Performance evaluation of fuzzy rule based networks.\n  Lecture Notes Available for download here.\n","date":1542247370,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1542247370,"objectID":"3ce7fc000ad72546bd0ac68a9f0bf5b8","permalink":"/eve-ann/tutorial/","publishdate":"2018-11-14T19:02:50-07:00","relpermalink":"/eve-ann/tutorial/","section":"eve-ann","summary":"Tutorial on Fuzzy Systems and Networks: Theory and Applications  The tutorial focuses on some theoretical concepts for fuzzy systems and networks in the context of model complexity attributes such as nonlinearity, dimensionality and structure. In particular, rule base reduction and compression methods for fuzzy systems are considered. Also, fuzzy networks with chained and modular rule bases are discussed. The theoretical results are applied to several case studies and validated comparatively using established metrics for model performance indicators such as accuracy, efficiency and transparency.","tags":null,"title":"Tutorial on Fuzzy Systems and Networks: Theory and Applications","type":"eve-ann"},{"authors":null,"categories":null,"content":"Video surveillance            The activity concerns the analysis, design and implementation of machine learning methods for the detection, tracking and real-time recognition of objects in motion sequences, also in mobile environments. Concerning real-time support, extensions of a video surveillance system have been proposed to make possible to guarantee speedup very close to the ideal, while improving the accuracy of the results detection. The extensions include the design of parallelization techniques at instruction-level, by SSE2, of the main computational cores and the real-time support to the operating systems to reduce jittering in video mobile transmission. Detection is dealt by proposing an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science. The approach, adopted as basis to model either background and foreground, can handle scenes containing moving backgrounds, camouflage and gradual illumination variations, can include into the background model shadows cast by moving objects, and achieves robust detection for different types of videos taken with stationary cameras. Moreover, for object tracking we propose an Artificial Intelligence approach to improve correct estimates, that suitably combines Particle filtering and a matching model belonging to the class of Multiple Hypothesis Testing.   \\     ","date":1542247370,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1542247370,"objectID":"eac7e5622702433d29a0aafc53861ae3","permalink":"/research/video/","publishdate":"2018-11-14T19:02:50-07:00","relpermalink":"/research/video/","section":"research","summary":"Video surveillance            The activity concerns the analysis, design and implementation of machine learning methods for the detection, tracking and real-time recognition of objects in motion sequences, also in mobile environments. Concerning real-time support, extensions of a video surveillance system have been proposed to make possible to guarantee speedup very close to the ideal, while improving the accuracy of the results detection.","tags":null,"title":"Video Surveillance","type":"research"},{"authors":null,"categories":null,"content":"Post-Doc of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         **CVPR Research Area:** Spatiotemporal Data Analysis, Spatiotemporal Uncertainty Management   Research Interests: Periodic Pattern Discovery, Outlier Detection, Rough Set, Granular Computing and Rough Dependency Rules.   E-mail: alessia.albanese@uniparthenope.it   Phone: +39 0815476656   Fax: +39 0815476514   Room: CVPR Lab - 4th Floor, North    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"bed57d9be8e5c90187e0cbff1e8b7411","permalink":"/staff/alumni/albanese/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/alumni/albanese/","section":"staff","summary":"Post-Doc of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         **CVPR Research Area:** Spatiotemporal Data Analysis, Spatiotemporal Uncertainty Management   Research Interests: Periodic Pattern Discovery, Outlier Detection, Rough Set, Granular Computing and Rough Dependency Rules.   E-mail: alessia.albanese@uniparthenope.it   Phone: +39 0815476656   Fax: +39 0815476514   Room: CVPR Lab - 4th Floor, North    ","tags":null,"title":"Alessia Albanese","type":"staff"},{"authors":null,"categories":null,"content":"Researcher in Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area: Image analysis - Soft computing   Research Interests: Soft computing - fuzzy and rough sets - pattern recognition for structured data   E-mail: alessio.ferone@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=29   Phone: +39 0815476656   Fax: +39 0815476514   Room: 428, IV Floor, North    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"b633150972845434e1eff69a346acc47","permalink":"/staff/internalstaff/ferone/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/internalstaff/ferone/","section":"staff","summary":"Researcher in Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area: Image analysis - Soft computing   Research Interests: Soft computing - fuzzy and rough sets - pattern recognition for structured data   E-mail: alessio.ferone@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=29   Phone: +39 0815476656   Fax: +39 0815476514   Room: 428, IV Floor, North    ","tags":null,"title":"Alessio Ferone","type":"staff"},{"authors":null,"categories":null,"content":" Michele Di Capua Alessia Albanese   ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"4a7b173687077551799da2f67a44b04f","permalink":"/staff/alumni/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/alumni/","section":"staff","summary":" Michele Di Capua Alessia Albanese   ","tags":null,"title":"Alumni","type":"staff"},{"authors":null,"categories":null,"content":"Associate Professor of Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area: Pattern recognition and machine learning (neural networks, fuzzy logic and statistical approaches)   Research Interests: Data analysis, classification and clustering (unstructured and structured data, signal processing and biological data)   E-mail: angelo.ciaramella@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=16   Phone: +39 0815476674   Fax: +39 0815476514   Room: 431, IV Floor, North    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"20ce5f735e3079819e2a2c0e4161d47b","permalink":"/staff/internalstaff/ciaramella/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/internalstaff/ciaramella/","section":"staff","summary":"Associate Professor of Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area: Pattern recognition and machine learning (neural networks, fuzzy logic and statistical approaches)   Research Interests: Data analysis, classification and clustering (unstructured and structured data, signal processing and biological data)   E-mail: angelo.ciaramella@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=16   Phone: +39 0815476674   Fax: +39 0815476514   Room: 431, IV Floor, North    ","tags":null,"title":"Angelo Ciaramella","type":"staff"},{"authors":null,"categories":null,"content":"Researcher of Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area: Cloud computing and cybersecurity   Research Interests: Cloud/Edge Computing, Security and Data Protection   E-mail: aniello.castiglione@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=123   Phone: +39 081547\u0026mdash;-   Fax:    Room: 413, IV Floor, South    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"674e4a31bfdd97981683239bf547addd","permalink":"/staff/internalstaff/castiglione/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/internalstaff/castiglione/","section":"staff","summary":"Researcher of Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area: Cloud computing and cybersecurity   Research Interests: Cloud/Edge Computing, Security and Data Protection   E-mail: aniello.castiglione@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=123   Phone: +39 081547\u0026mdash;-   Fax:    Room: 413, IV Floor, South    ","tags":null,"title":"Aniello Castiglione","type":"staff"},{"authors":null,"categories":null,"content":"Researcher of Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area:    Research Interests:    E-mail: antonino.staiano@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=76   Phone: +39 0815476520   Fax: +39 0815476514   Room: 431, IV Floor, North    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"df9d1d2bdbcdd7ba4d0abde23e442b5d","permalink":"/staff/internalstaff/staiano/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/internalstaff/staiano/","section":"staff","summary":"Researcher of Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area:    Research Interests:    E-mail: antonino.staiano@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=76   Phone: +39 0815476520   Fax: +39 0815476514   Room: 431, IV Floor, North    ","tags":null,"title":"Antonino Staiano","type":"staff"},{"authors":null,"categories":null,"content":"Researcher of Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area: Structured Pattern Recognition, Biological and biomedical data and image analysis   Research Interests: Data Mining, Bioinformatics, Web Analysis   E-mail: antonio.maratea@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=43   Phone: +39 0815476687   Fax: +39 0815476514   Room: 406\\    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"3bf95e53349b1df1d058af82019a788d","permalink":"/staff/internalstaff/maratea/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/internalstaff/maratea/","section":"staff","summary":"Researcher of Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area: Structured Pattern Recognition, Biological and biomedical data and image analysis   Research Interests: Data Mining, Bioinformatics, Web Analysis   E-mail: antonio.maratea@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=43   Phone: +39 0815476687   Fax: +39 0815476514   Room: 406\\    ","tags":null,"title":"Antonio Maratea","type":"staff"},{"authors":null,"categories":null,"content":"Machine Learning for Audio, Image and Video Analysis\nCamastra Francesco, Vinciarelli Alessandro\nHandbook on Soft Computing for Video Surveillance\nS.K. Pal, A. Petrosino, L. Maddalena (Eds), Chapman \u0026amp; Hall/CRC\nISBN: 9781439856840, 2012.\\\nFuzzy Logic and Applications, Lecture Notes in Computer Science, vol. 6857\nAnna Maria Fanelli, Witold Pedrycz, Alfredo Petrosino (Eds.), Springer 2011, isbn 978-3-642-23712-6\\\nFuzzy Logic and Applications, Lecture Notes in Computer Science, vol. 3849\nBloch, Isabelle; Petrosino, Alfredo; Tettamanzi, Andrea G.B. (Eds.), Springer-Verlag, 2006, ISBN: 3-540-32529-8\\\nFuzzy Logic and Applications, Lecture Notes in Computer Science, vol. 2955\nDi Gesù, Vito; Masulli, Francesco; Petrosino, Alfredo (Eds.), 2005\nISBN: 3-540-31019-3.\\\nVisual Attention Mechanisms\nCantoni V., Marinaro M., Petrosino A., Ed.s, Kluwer, 2002.\\\nNew Trends in Fuzzy Logic II\nCastellano M., Blonda P., Petrosino A. Ed.s, World Scientific Publishing, Singapore,1998.\\\nNew Trends in Fuzzy Logic\nBonarini A., Mancini A., Masulli F., Petrosino A. Ed.s, World Scientific Publishing, Singapore, 1996\\\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"605cbd23d5eb50fbe8af6a5e22fed1ce","permalink":"/publications/books/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/publications/books/","section":"publications","summary":"Machine Learning for Audio, Image and Video Analysis\nCamastra Francesco, Vinciarelli Alessandro\nHandbook on Soft Computing for Video Surveillance\nS.K. Pal, A. Petrosino, L. Maddalena (Eds), Chapman \u0026amp; Hall/CRC\nISBN: 9781439856840, 2012.\\\nFuzzy Logic and Applications, Lecture Notes in Computer Science, vol. 6857\nAnna Maria Fanelli, Witold Pedrycz, Alfredo Petrosino (Eds.), Springer 2011, isbn 978-3-642-23712-6\\\nFuzzy Logic and Applications, Lecture Notes in Computer Science, vol. 3849\nBloch, Isabelle; Petrosino, Alfredo; Tettamanzi, Andrea G.","tags":null,"title":"Books","type":"publications"},{"authors":null,"categories":null,"content":"Compressive Sensing and Hierarchical Clustering for Microarray Data with Missing Values, A. Ciaramella, D. Nardone, A. Staiano, Lecture Notes in Bioinformatics, “Computational Intelligence Methods for Bioinformatics and Biostatistics”, ISBN 978-3-030-34585-3;\nSemantic Maps for Knowledge Management of Web and Social Information, Camastra, F., Ciaramella, A., Maratea, A., Son, L.H., Staiano, A. (2020) Studies in Computational Intelligence, 837, pp. 39-51\nBlind Source Separation Using Dictionary Learning in Wireless Sensor Network Scenario, A. Ciaramella, D. Nardone, A. Staiano (2020) . In: Esposito A., Faundez-Zanuy M., Morabito F., Pasero E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore;\nFuzzy Similarity-based Hierarchical Clustering for Atmospheric Pollutants Prediction, F. Camastra, A. Ciaramella, A. Riccio, S. Le Hoang, A. Staiano, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11291 LNAI, pp. 123-133, 2019;\nContent-based music agglomeration by sparse modeling and convolved independent component analysis, M. Iannicelli, D. Nardone, A. Ciaramella, A. Staiano, (2019) Smart Innovation, Systems and Technologies, 103, pp. 87-96;\nFuzzy clustering of structured data: Some preliminary results, G. Vettigli, A. Ciaramella, IEEE International Conference on Fuzzy Systems, art. no. 8015648, 2017;\nOn the estimation of pollen density on non-target lepidoptera food plant leaves in bt-maize exposure models: Open problems and possible neural network-based solutions, F. Camastra, A. Ciaramella, A. Staiano, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10613 LNCS, pp.407-414, 2017;\nSemantic maps of Twitter conversations, A. Ciaramella, A. Maratea, E. Spagnoli, Smart Innovation, Systems and Technologies, 69, pp. 327-338, 2017;\nA bayesian-based neural network model for solar photovoltaic power forecasting, A. Ciaramella, A. Staiano, G. Cervone, S. Alessandrini, Smart Innovation, Systems and Technologies, 54, pp. 169-177, ISSN: 21903018, doi: 10.1007/978-3-319-33747-0_17, 2016;\nA Fuzzy Decision Support System for the Environmental Risk Assessment of genetically modified organisms, F. Camastra, A. Ciaramella, V. Giovannelli, M. Lener, V. Rastelli, A. Staiano, G. Staiano, A. Starace,Smart Innovation, Systems and Technologies, 26,pp. 241-249, ISSN: 21903018, doi: 10.1007/978-3-319-04129-2_24, 2014;\nEnvironmental Risk Assessment of Genetically Modified Organisms by a Fuzzy Decision Support System, F. Camastra, A. Ciaramella, V. Giovannelli, M. Lener, V. Rastelli, A. Staiano, G. Staiano, A. Starace, LNCS, vol. 8158, p. 428-435, ISBN: 978-3-642-41189-2, ISSN:0302-9743, doi: 10.1007/978-3-642-41190-846, 2013;\nRule Learning in a Fuzzy Decision Support System for the Environmental Risk Assessment of GMOs, F. Camastra, A. Ciaramella, V. Giovannelli, M. Lener, V. Rastelli, A. Staiano, G. Staiano, A. Starace, LNAI, vol. 8256, p. 226-233, ISBN: 978-3-319-03199-6, ISSN:0302-9743, doi: 10.1007/978-3-319-03200-923, 2013;\nComparison of Dispersion Models by Using Fuzzy Similarity Relations, A. Ciaramella, A. Riccio, S. Galmarini, G. Giunta, S. Potempski, LNCS, vol. 6934; p. 57-67, ISSN: 0302-9743, doi:10.1007/978-3-642-23954-0_8, 2011;\nUninorm Based Fuzzy Network for Tree Data Structures, A. Ciaramella, W. Pedrycz, A. Petrosino, LNAI 5571, pp. 77-84, ISSN: 0302-9743, doi: 10.1007/978-3-642-02282-1_10, 2009;\nStatistical and Fuzzy Approaches for Atmospheric Boundary Layer Classification, A. Ciaramella, A. Riccio, F. Angelini, G. P. Gobbi, T. C. Landi, LNAI 5853, pp. 375-384, ISSN: 0302-9743, doi:10.1007/978-3-642-10291-2_38, 2009;\nIndependent Data Model Selection for Ensemble Dispersion Forecasting, A. Ciaramella, G. Giunta, A. Riccio, S. Galmarini, Book: Applications of Supervised and Unsupervised Ensemble Methods Series: Studies in Computational Intelligence, Vol. 245, pp. 213-231, ISSN: 1860-949X, doi: 10.1007/978-3-642-03999-7_12, 2009;\nSingle Channel Polyphonic Music Transcription, A. Ciaramella,Frontiers in Artificial Intelligence and Applications (IOS), vol. 193, pp. 99-108, ISSN: 0922-6389, doi:10.3233/978-1-58603-984-4-99, 2009\nThe Genetic Development of Uninorm-Based Neurons, A. Ciaramella, W. Pedrycz and R. Tagliaferri, LNAI 4578, pp. 69-76, ISSN: 0302-9743, 2007;\nClustering, Assessment and Validation: an application to gene expression data, A. Ciaramella, S. Cocozza, F. Iorio, G. Miele, F. Napolitano, M. Pinelli, G. Raiconi, R. Tagliaferri, Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA,August 12-17, 2007, pp. 1419-1425, ISSN: 10987576, doi: 10.1109/IJCNN.2007.4371199, 2007;\nNEC for Gene Expression Analysis, R. Amato, A. Ciaramella, N. Deniskina, et al., LNAI 3849 , pp. 246-251, ISSN: 03029743, doi:10.1007/11676935_30, 2006;\nOR/AND Neurons for Fuzzy Set Connectives Using Ordinal Sums and Genetic Algorithms, A. Ciaramella, W. Pedrycz, R. Tagliaferri, LNAI 3849, pp. 188-194, ISSN:0302-9743, doi: 10.1007/11676935_23, 2006;\nNEC: a Hierarchical Agglomerative Clustering Based on Fisher and Negentropy Information, A. Ciaramella, G. Longo, A. Staiano, R. Tagliaferri, LNCS 3931, pp. 49-56, ISSN: 03029743, doi: 10.1007/11731177_8, 2006;\nFuzzy Relational Neural Network for Data Analysis, A. Ciaramella, W. Pedrycz, R. Tagliaferri, A. Di Nola, LNAI 2955, pp. 103 - 109, ISSN: 0302-9743, 2006;\nBSS Toolbox for Delayed and Convolved Mixtures, A. Ciaramella, F. Iorio, R. Tagliaferri, Proceedings of IEEE International Joint Conference on Neural Networks 2005 (IJCNN05), vol. 2, pp. 1245 - 1250,ISBN: 0780390482;978-078039048-5, doi: 10.1109 /IJCNN.2005.1556032,2005;\nData Visualization Methodologies for Data Mining Systems in Bioinformatics, A. Ciaramella, A. Staiano, R. Tagliaferri et al., Proceedings of IEEE International Joint Conference on Neural Networks 2005 (IJCNN05), vol. 1, pp. 143 - 148, ISBN: 0780390482;978-078039048-5, doi:10.1109 /IJCNN.2005.1555820, 2005;\nVisualization, Clustering and Classification of Multidimensional Astronomical Data, A. Ciaramella, A. Staiano, R. Tagliaferri et al., Proceedings of IEEE International Workshop onComputer Architecture for Machine Perception (CAMP05 ), pp. 141 - 146, 2005;\nInference Systems by Using Ordinal Sums and Genetic Algorithms, A. Ciaramella, W. Pedrycz, R. Tagliaferri, Proceedings of NAFIPS 2004, IEEE Annual Meeting of the Fuzzy Information, vol.2, pp. 629 - 634, 2004;\nFuzzy Neural Networks Based on Fuzzy Logic Algebras Valued Relations, R. Tagliaferri, A. Ciaramella, A. Di Nola, R. Belohlavek,“Fuzzy Partial Differential Equations and Relational Equations: Reservoir Characterization and Modeling”, M. Nikravesh,L.A. Zadeh, V. Korotnihk (Eds.), Springer-Verlag, ISBN: 978-3-540-20322-3, doi: 10.1007/978-3-540-39675-8_3, 2004;\nOrdinal Sums by Using Genetic Algorithms , A. Ciaramella, W. Pedrycz, R. Tagliaferri, Proceedings of FUZZ-IEEE 2004, IEEE International Conference on Fuzzy Systems, vol. 2, pp. 641-646, ISSN: 10987584, doi: 10.1109 /FUZZY.2004.1375472, 2004;\nICA for Modelling and Generating Organ Pipes Self-sustained Tones, A. Ciaramella, E. De Lauro, S. De Martino, M. R. Falanga, R. Tagliaferri, Proceedings of IJCNN 2004, IEEE International Joint Conference on Neural Networks, pp. 261-266, ISSN: 10987576, 2004;\nProbabilistic principal surfaces for yeast gene microarray data mining, A. Staiano, L. De Vinco, A. Ciaramella, G. Raiconi, R. Tagliaferri, R. Amato, G. Longo, C. Donalek, G. Miele, D.D. Bernardo, Proceedings of IEEE Conference on Data Mining, Brighton, UK, 1-4 Novembre, ISBN: 0769521428;978-076952142-8, doi: 10.1109/ICDM.2004.10088, 2004;\nAmplitude and Permutation Indeterminacies in Frequency Domain Convolved ICA, A. Ciaramella, R. Tagliaferri, Proceedings of the IEEE International Joint Conference on Neural Networks 2003, vol. 1, pp. 708-713, 2003;\nFuzzy Similarities in Stars/Galaxies Classification , S. Sessa , R. Tagliaferri, G. Longo, A. Ciaramella, A. Staiano, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 2 , pp. 494-496, ISSN: 08843627, 2002;\nFuzzy relations neural network: Some preliminary results, A. Ciaramella, W. Pedrycz, R. Tagliaferri, Proceedings of the 10th IEEE International Conference on Fuzzy Systems, vol. 1, pp. 469-472, 2001;\nTwo-layer Fuzzy Relational Networks: some preliminary results, A. Ciaramella, W. Pedrycz, R. Tagliaferri, Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems \u0026amp; Thecnology (CBGI) 2001, pp. 82-86, ISBN: 0970789009, 2001;\nAdvanced Data Mining Tools for Exploring Large Astronomical Data Bases, G. Longo, R. Tagliaferri, S. Sessa, P. Ortiz, M. Capaccioli, A. Ciaramella, C. Donalek, G. Raiconi, A. Staiano, A. Volpicelli, SPIE’s 46th Annual Meeting International Symposium on Optical Scienceand Technology, pp. 61-75, ISSN: 0277786X, doi: 10.1117/12.447191, 2001;\nHybrid Neural Networks for Frequency Estimation of Unevenly Sampled Data, F. Barone, A. Ciaramella, L. Milano, R. Tagliaferri, G. Longo, Proceedings of the International JointConference on Neural Networks (IJCNN), vol. II, pp. 975-979, 2000;\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"dbff1d3db148f84f2bfde510b9e3879d","permalink":"/publications/conference/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/publications/conference/","section":"publications","summary":"Compressive Sensing and Hierarchical Clustering for Microarray Data with Missing Values, A. Ciaramella, D. Nardone, A. Staiano, Lecture Notes in Bioinformatics, “Computational Intelligence Methods for Bioinformatics and Biostatistics”, ISBN 978-3-030-34585-3;\nSemantic Maps for Knowledge Management of Web and Social Information, Camastra, F., Ciaramella, A., Maratea, A., Son, L.H., Staiano, A. (2020) Studies in Computational Intelligence, 837, pp. 39-51\nBlind Source Separation Using Dictionary Learning in Wireless Sensor Network Scenario, A. Ciaramella, D.","tags":null,"title":"Conference Papers","type":"publications"},{"authors":null,"categories":null,"content":"Digital Film Restoration  Activities concern several aspects of digital film restoration, including the analysis of issues related to the problem, ranging from the kind of different defects, to their causes, and to methods and algorithms for their removal. Particular attention is given to some specific types of defects that can affect digital image sequences and to methodologies adopted for their management, devising new machine learning based algorithms and methodologies for their removal. Defects taken into consideration include dust and dirt and linear scratches.\nWe have proposed methods for automatic removal of linear scratches in digital image sequences, based on the idea of adopting an image model as simple as possible, evaluate the displacement of such model from the real model, and correct scratch removal through the addition of the computed displacement.\nMoreover, we devised a method for the detection and the removal of linear blue scratches that affect also modern color movies, based on specific characteristics of such kind of defect.\nWe also proposed a new methodology for the solution of classes of problems related to digital film restoration that is well suited for implementation into high-performance parallel and distributed computing environments. The basic idea is to adopt several well settled algorithms for the class of problems at hand, and to combine obtained results through the adoption of suitable image fusion techniques, with the aim of taking advantage of adopted algorithms potentialities and at the same time reducing their disadvantages.\nFinally, for dust and blotch removal, a novel approach was envisaged, based on viewing the problem as one of separating overlapping images, and then reformulating it as a Blind Separation problem, approached through Independent Component Analysis techniques. See links to: - GC06BlueScratches: Page created in order to show the images used for testing of the blue scratch detection and removal algorithms presented in [7] and [8].\n BlueScratches: Page created in order to show the images used for testing of the blue scratch detection and removal algorithms presented in [8]. - DataFusionScratches: Page created in order to show the images used for testing of the scratch detection and removal algorithm presented in [9].  Papers on Digital Film Restoration [1] L. Maddalena, Methods for Scratch Removal in Image Sequences , in Proceedings of 11th International Conference on Image Analysis and Processing (ICIAP2001), IEEE Computer Society, ISBN 0-7695-1183-X, DOI 10.1109/ICIAP.2001.957067, pp 547-552, 2001.\n[2] G. Laccetti, L. Maddalena, A. Petrosino, Parallel/Distributed Film Line Scratch Restoration by Fusion Techniques, A. Laganà et al. (eds.), “Computational Science and its Applications – ICCSA 2004”, Lecture Notes in Computer Science, n. 3044, Springer, ISBN 3-540-22056-9, DOI 10.1007/b98051, pp. 524-534, 2004.\n[3] G. Laccetti, L. Maddalena, A. Petrosino, P-LSR: A Parallel Algorithm for Line Scratch Restoration, in Proceedings of the Seventh International Workshop on Computer Architecture for Machine Perception (CAMP2005), IEEE Computer Society, ISBN 0-7695-2255-6, pp. 225-230, 2005.\n[4] G. Laccetti, L. Maddalena, A. Petrosino, Removing Line Scratches in Digital Image Sequences by Fusion Techniques, in F. Roli e S. Vitulano (eds), 13th International Conference on Image Analysis and Processing (ICIAP2005), Lecture Notes in Computer Science, n. 3617, Springer-Verlag Berlin Heidelberg, pp. 695-702, DOI 10.1007/11553595_85, 2005.\n[5] L. Maddalena, A. Petrosino, A New Methodology for Line Scratch Restoration, in Summaries of \u0026ldquo;VIII Congresso Nazionale della SIMAI\u0026rdquo;, p. 210, 2006.\n[6] L. Maddalena, Recent Developments in Digital Film Restoration, in C. D’Amico (Ed.), Innovazioni Tecnologiche per i Beni Culturali in Italia, Patron Editore, ISBN 88-555-2886-6, 2006.\n[7] L. Maddalena, A. Petrosino, A Comparison of Algorithms for Blue Scratch Removal in Digital Images, in A. Rizzi (Ed.), Colore e colorimetria: contributi multidisciplinari, vol. II, SIOF, ISBN-10 88-7957-252-0, pp. 133-144, 2006.\n[8] L. Maddalena, A. Petrosino, Restoration of Blue Scratches in Digital Image Sequences, Image and Vision Computing, Vol. 26, Elsevier, The Netherlands, pagg. 1314–1326, 2008.\n[9] L. Maddalena, A. Petrosino, G. Laccetti, A Fusion-based Approach to Digital Movie Restoration, Pattern Recognition, DOI 10.1016/j.patcog.2008.10.026, Vol. 42, no. 7, pagg. 1485-1495, 2009.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"7c84040b70d7e661f7d4ed21258a7f22","permalink":"/research/digital/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/research/digital/","section":"research","summary":"Digital Film Restoration  Activities concern several aspects of digital film restoration, including the analysis of issues related to the problem, ranging from the kind of different defects, to their causes, and to methods and algorithms for their removal. Particular attention is given to some specific types of defects that can affect digital image sequences and to methodologies adopted for their management, devising new machine learning based algorithms and methodologies for their removal.","tags":null,"title":"Digital Film Restoration","type":"research"},{"authors":null,"categories":null,"content":"PhD of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area: Computer Vision and Pattern Recognition, Machine Learning   Research Interests: Emotion Recognition from Videos using Deep Learning, Modeling Deep Architectures for CNN, Object Detection and Tracking   E-mail: emanuel.dinardo@uniparthenope.it   Homepage: https://www.researchgate.net/profile/Emanuel_Di_Nardo   Phone: +39 0815476656   Fax: +39 0815476614   Room: CVPRlab - 4th Floor, North    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"da1b1fbf98075ce5a02faf7f87f32f19","permalink":"/staff/phdstudents/dinardo/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/phdstudents/dinardo/","section":"staff","summary":"PhD of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area: Computer Vision and Pattern Recognition, Machine Learning   Research Interests: Emotion Recognition from Videos using Deep Learning, Modeling Deep Architectures for CNN, Object Detection and Tracking   E-mail: emanuel.dinardo@uniparthenope.it   Homepage: https://www.researchgate.net/profile/Emanuel_Di_Nardo   Phone: +39 0815476656   Fax: +39 0815476614   Room: CVPRlab - 4th Floor, North    ","tags":null,"title":"Emanuele Di Nardo","type":"staff"},{"authors":null,"categories":null,"content":"Researcher of Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area: Pattern recognition and machine learning   Research Interests: Machine and Deep Learning, Computer Vision, Pattern Recognition, Biometrics, Virtual/Augmented Reality   E-mail: fabio.narducci@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=115   Phone: +39 0815476580   Fax:    Room: 428, IV Floor, North side    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"c8443ff998e86bf33767db0f438736dd","permalink":"/staff/internalstaff/narducci/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/internalstaff/narducci/","section":"staff","summary":"Researcher of Computer ScienceDepartment of Science and Technology, University of Naples Parthenope         CVPR Research Area: Pattern recognition and machine learning   Research Interests: Machine and Deep Learning, Computer Vision, Pattern Recognition, Biometrics, Virtual/Augmented Reality   E-mail: fabio.narducci@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=115   Phone: +39 0815476580   Fax:    Room: 428, IV Floor, North side    ","tags":null,"title":"Fabio Narducci","type":"staff"},{"authors":null,"categories":null,"content":"Researcher of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area: Machine Learning   Research Interests: Kernel Methods, Manifold Learning, Unsupervised Learning   E-mail: francesco.camastra@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=12   Phone: +39 0815476505   Fax: +39 0815476514   Room: 430, IV Floor, North    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"be8786a6baa976b06d166af855164761","permalink":"/staff/internalstaff/camastra/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/internalstaff/camastra/","section":"staff","summary":"Researcher of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area: Machine Learning   Research Interests: Kernel Methods, Manifold Learning, Unsupervised Learning   E-mail: francesco.camastra@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=12   Phone: +39 0815476505   Fax: +39 0815476514   Room: 430, IV Floor, North    ","tags":null,"title":"Francesco Camastra","type":"staff"},{"authors":null,"categories":null,"content":"PhD of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area: Computer Vision, Pattern Recognition, Parallell Computing   Research Interests: Background Modeling, CUDA , Path Planning for mobile robots   E-mail: giorgio.gemignani@uniparthenope.it   Homepage: http://www.dsi.unimi.it/persona.php?z=0\u0026amp;id=593   Phone: +39 0815476656   Fax: +39 0815476614   Room: CVPRlab - 4th Floor, North    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"b65be6d84a8a9b207e7e66078946256d","permalink":"/staff/phdstudents/gemignani/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/phdstudents/gemignani/","section":"staff","summary":"PhD of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area: Computer Vision, Pattern Recognition, Parallell Computing   Research Interests: Background Modeling, CUDA , Path Planning for mobile robots   E-mail: giorgio.gemignani@uniparthenope.it   Homepage: http://www.dsi.unimi.it/persona.php?z=0\u0026amp;id=593   Phone: +39 0815476656   Fax: +39 0815476614   Room: CVPRlab - 4th Floor, North    ","tags":null,"title":"Giorgio Gemignani","type":"staff"},{"authors":null,"categories":null,"content":"PhD of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area: Computer Vision and Pattern Recognition, Machine Learning   Research Interests: Action Recognition from Videos using Deep Learning, Modeling Deep Architectures for CNN, Object Detection and Tracking   E-mail: ihsan.ullah@uniparthenope.it   Homepage:    Phone: +39 0815476656   Fax: +39 0815476614   Room: CVPRlab - 4th Floor, North    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"526ee17e91773e9e487d26b7896e9f96","permalink":"/staff/phdstudents/ullah/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/phdstudents/ullah/","section":"staff","summary":"PhD of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area: Computer Vision and Pattern Recognition, Machine Learning   Research Interests: Action Recognition from Videos using Deep Learning, Modeling Deep Architectures for CNN, Object Detection and Tracking   E-mail: ihsan.ullah@uniparthenope.it   Homepage:    Phone: +39 0815476656   Fax: +39 0815476614   Room: CVPRlab - 4th Floor, North    ","tags":null,"title":"Ihsan Ullah Afredi","type":"staff"},{"authors":null,"categories":null,"content":"     Head of Laboratory\nAngelo Ciaramella\n     Deputy Head of Laboratory\nAntonio Maratea\n     Tenured Associate Professor\nFrancesco Camastra\n     Tenured Assistant Professor\nRaffaele Montella      Tenured Assistant Professor\nAntonino Staiano      Tenured Assistant Professor\nAlessio Ferone      Fixed Term Assistant Professor\nAniello Castiglione  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"abaf5c19fb67787b35cf75ead19693ee","permalink":"/staff/internalstaff/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/internalstaff/","section":"staff","summary":"Head of Laboratory\nAngelo Ciaramella\n     Deputy Head of Laboratory\nAntonio Maratea\n     Tenured Associate Professor\nFrancesco Camastra\n     Tenured Assistant Professor\nRaffaele Montella      Tenured Assistant Professor\nAntonino Staiano      Tenured Assistant Professor\nAlessio Ferone      Fixed Term Assistant Professor","tags":null,"title":"Internal Staff","type":"staff"},{"authors":null,"categories":null,"content":"Antonio Maratea, Angelo Ciaramella, Giuseppe Pio Cianci, Record linkage of banks and municipalities through multiple criteria and neural networks, PeerJ Computer Science 6:e258 https://doi.org/10.7717/peerj-cs.258, 2020\nDavide Nardone, Andelo Ciaramella, Antonino Staiano, A Sparse-Modeling Based Approach for Class Specific Feature Selection, PeerJ Computer Science, 5:e237, doi.org/10.7717/peerj-cs.237, 2019\nAngelo Ciaramella, Antonino Staiano, On the Role of Clustering and Visualization Techniques in Gene Microarray Data, A. Ciaramella, A. Staiano, Algorithms, 12(6), 123, 2019\nAlessio Ferone, Antonio Maratea: Integrating rough set principles in the graded possibilistic clustering. Information Sciences. 477: 148-160 (2019)\nElena Chianese, Francesco Camastra, Angelo Ciaramella, Tony Christian Landi, Antonino Staiano, Angelo Riccio: Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron. Ecological Informatics 49: 54-61 (2019)\nSilvio Barra, Maria De Marsico, Michele Nappi, Fabio Narducci, Daniel Riccio: A hand-based biometric system in visible light for mobile environments. Information Sciences. 479: 472-485 (2019)\nBattistone, Francesco, Alfredo Petrosino: TGLSTM: A time based graph deep learning approach to gait recognition. Pattern Recognition Letters (in press).\nFreire-Obregón, D., Narducci, F., Barra, S., Castrillón-Santana, M.: Deep learning for source camera identification on mobile devices. Pattern Recognition Letters (in press)\nL. Maddalena and A. Petrosino, Self-Organizing Background Subtraction Using Color and Depth Data, Multimedia Tools and Applications, Springer, (in press).\nLucia Maddalena, Alfredo Petrosino, Background Subtraction for Moving Object Detection in RGBD Data: A Survey. J. Imaging 4(5): 71 (2018)\nFrancesco Battistone, Alfredo Petrosino, Vincenzo Santopietro, Watch Out: Embedded Video Tracking with BST for Unmanned Aerial Vehicles. Signal Processing Systems 90(6): 891-900 (2018)\nFrancesco Camastra, Francesco Esposito, Antonino Staiano: Linear SVM-based recognition of elementary juggling movements using correlation dimension of Euler Angles of a single arm. Neural Computing and Applications 29(11): 1005-1013 (2018)\nAlessio Ferone: Feature selection based on composition of rough sets induced by feature granulation. Internatioanl Journal Approximate Reasoning 101: 276-292 (2018)\nSilvio Barra, Kim-Kwang Raymond Choo, Michele Nappi, Arcangelo Castiglione, Fabio Narducci, Rajiv Ranjan: Biometrics-as-a-Service: Cloud-Based Technology, Systems, and Applications. IEEE Cloud Computing 5(4): 33-37 (2018)\nMaria De Marsico, Michele Nappi, Fabio Narducci, Hugo Proença: Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation. Pattern Recognition 74: 286-304 (2018)\nAntonio Maratea, Alfredo Petrosino, Mario Manzo: User Click Modeling on a Learning Management System. IJHCITP 8(4): 38-49 (2017)\nAlfredo Petrosino, Lucia Maddalena, Thierry Bouwmans: Editorial-Scene background modeling and initialization. Pattern Recognition Letters 96: 1-2 (2017)\nThierry Bouwmans, Lucia Maddalena, Alfredo Petrosino: Scene background initialization: A taxonomy. Pattern Recognition Letters 96: 3-11 (2017)\nJodoin P-M, Maddalena L., Petrosino A., Wang Y., Extensive Benchmark and Survey of Background Modeling Methods. IEEE Transactions on Image Processing 26 (11) : 5244-5256 (2017)\nAniello Castiglione, Kim-Kwang Raymond Choo, Michele Nappi, Fabio Narducci: Biometrics in the Cloud: Challenges and Research Opportunities. IEEE Cloud Computing 4(4): 12-17 (2017)\nAndrea F. Abate, Silvio Barra, Luigi Gallo, Fabio Narducci: Kurtosis and skewness at pixel level as input for SOM networks to iris recognition on mobile devices. Pattern Recognition Letters 91: 37-43 (2017)\nH. Fu, Y. Wei, F. Camastra, P. Aricò, H. Sheng, Advances in Eye Tracking Technology: Theory, Algorithms, and Applications. Comp. Int. and Neurosc. 2016: 7831469:1-7831469:2 (2016)\nF. Camastra, A. Staiano, Intrinsic dimension estimation: Advances and open problems. Information Sciences. 328: 26-41 (2016)\nM. De Marsico, A. Petrosino, S. Ricciardi, Iris recognition through machine learning techniques: A survey. Pattern Recognition Letters 82: 106-115 (2016)\nAngelo Ciaramella, Giulio Giunta: Packet loss recovery in audio multimedia streaming by using compressive sensing. IET Communications 10(4): 387-392 (2016)\nAngelo Ciaramella, Marco Gianfico, Giulio Giunta: Compressive sampling and adaptive dictionary learning for the packet loss recovery in audio multimedia streaming. Multimedia Tools Appl. 75(24): 17375-17392 (2016)\nJoão C. Neves, Fabio Narducci, Silvio Barra, Hugo Proença: Biometric recognition in surveillance scenarios: a survey. Artificial Intelligence Review 46(4): 515-541 (2016)\nFabio Narducci, Stefano Ricciardi, Raffaele Vertucci: Enabling consistent hand-based interaction in mixed reality by occlusions handling. Multimedia Tools Appl. 75(16): 9549-9562 (2016)\nS. Iodice, A. Petrosino, Salient feature based graph matching for person re-identification. Pattern Recognition 48(4): 1074-1085 (2015)\nA. Petrosino, Special Section: ICIAP 2013 Awards. Pattern Recognition Letters 55: 34 (2015)\nF. Camastra, R. Amato, M. D. Di Taranto, A. Staiano, Advances in Computational Methods for Genetic Diseases. Comp. Math. Methods in Medicine 2015: 645649:1-645649:2 (2015)\nF. Camastra, M. D. Di Taranto, A. Staiano, Statistical and Computational Methods for Genetic Diseases: An Overview. Comp. Math. Methods in Medicine 2015: 954598:1-954598:8 (2015)\nF. Camastra, A. Ciaramella, V. Giovannelli, M. Lener, V. Rastelli, A. Staiano, G. Staiano, A. Starace, A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference. Expert Syst. Appl. 42(3): 1710-1716 (2015)\nFrancesco Camastra, Angelo Ciaramella, Valeria Giovannelli, Matteo Lener, Valentina Rastelli, Antonino Staiano, Giovanni Staiano, Alfredo Starace: A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference. Expert Syst. Appl. 42(3): 1710-1716 (2015)\nSilvio Barra, Andrea Casanova, Fabio Narducci, Stefano Ricciardi: Ubiquitous iris recognition by means of mobile devices. Pattern Recognition Letters 57: 66-73 (2015)\nA. Ferone, L. Maddalena, Neural Background Subtraction for Pan-Tilt-Zoom Cameras. IEEE Trans. Systems, Man, and Cybernetics: Systems 44(5): 571-579 (2014)\nL. Maddalena, A. Petrosino, The 3dSOBS+ algorithm for moving object detection. Computer Vision and Image Understanding 122: 65-73 (2014)\nA. Maratea, A. Petrosino, M. Manzo, Adjusted F-measure and kernel scaling for imbalanced data learning. Informaiton Sciences 257: 331-341 (2014)\nL. Maddalena, A. Petrosino, F. Russo, People counting by learning their appearance in a multi-view camera environment. Pattern Recognition Letters 36: 125-134 (2014)\nA. Albanese, S. K. Pal, A. Petrosino, Rough Sets, Kernel Set, and Spatiotemporal Outlier Detection. IEEE Trans. Knowl. Data Eng. 26(1): 194-207 (2014)\nA. Petrosino, S. K. Pal, Guest Editorial on Decision Making in Human and Machine Vision. IEEE Trans. Systems, Man, and Cybernetics: Systems 44(5): 521-522 (2014)\nFrancesco Camastra, Angelo Ciaramella, Valeria Giovannelli, Matteo Lener, Valentina Rastelli, Antonino Staiano, Giovanni Staiano, Alfredo Starace: TÉRA: A tool for the environmental risk assessment of genetically modified plants. Ecological Informatics 24: 186-193 (2014)V. Cantoni, A. Ferone, O. Ozbudak, A. Petrosino, Protein motifs retrieval by SS terns occurrences, Pattern Recognition Letters 34(5): 559-563 (2013) - PDF - BibTeX\nR. Melfi, S. Kondra, A. Petrosino, Human activity modeling by spatio temporal textural appearance. Pattern Recognition Letters 34(15): 1990-1994 (2013)\nL. Maddalena, A. Petrosino, Stopped Object Detection by Learning Foreground Model in Videos. IEEE Trans. Neural Netw. Learning Syst. 24(5): 723-735 (2013)\nF. Camastra, A. Ciaramella, A. Staiano, Machine learning and soft computing for ICT security: an overview of current trends. J. Ambient Intelligence and Humanized Computing 4(2): 235-247 (2013)\nL. Lamberti, F. Camastra, Handy: A real-time three color glove-based gesture recognizer with learning vector quantization, Expert Syst. Appl. 39(12): 10489-10494 (2012) - PDF - BibTex\nA. Ferone, L. Maddalena, Neural Background Subtraction for PTZ Cameras, IEEE Transactions on Systems, Man, and Cybernetics: Systems, accepted - [PDF](/pdf/Neural Background Subtraction for PTZ Cameras.pdf) - BibTeX\nA. Albanese, S. K. Pal, A. Petrosino, Rough Sets, Kernel Set and Spatio-Temporal Outlier Detection, IEEE Transactions on Knowledge and Data Engineering(99): 1 (2012) - [PDF](/pdf/Rough Sets, Kernel Set and Spatio-Temporal Outlier Detection.pdf) - [BibTeX](/bib/Rough Sets, Kernel Set and Spatio-Temporal Outlier Detection.bib)\nA. Petrosino, M. Miralto, A. Ferone, A real-time streaming server in the RTLinux environment using VideoLanClient, J. Real-Time Image Processing 6(4): 247-256 (2011) - [PDF](/pdf/A real-time streaming server in the RTLinux environment using VideoLanClient.pdf) - [BibTeX](/bib/A real-time streaming server in the RTLinux environment using VideoLanClient.bib)\nA. Ciaramella, E. De Lauro, S. De Martino, M. Falanga, R. Tagliaferri: Modeling and Generating Organ Pipes Self-Sustained Tones by Using ICA. J. Signal and Information Processing 2(3): 141-151 (2011) - PDF - BibTeX\nL. Maddalena, A. Petrosino, A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection, Neural Computing and Applications 19(2): 179-186 (2010) - [PDF](/pdf/A Fuzzy Spatial Coherence based Approach to Background Foreground Separation for Moving Object Detection.pdf) - [BibTeX](/bib/A Fuzzy Spatial Coherence based Approach to Background Foreground Separation for Moving Object Detection.bib)\nG. Calcagno, A. Staiano, G. Fortunato, V. Brescia-Morra, E. Salvatore, R. Liguori, S. Capone, A. Filla, G. Longo, L. Sacchetti, A multilayer perceptron neural network-based approach for the identification of responsiveness of interferon therapy in multiple sclerosis patients, Information Sciences, Vol. 180, Issue 21, pp. 4153-4163, (2010) - PDF - BibTeX\nL. Maddalena, A. Petrosino, \u0026ldquo;A Fuzzy Spatial Coherence-based Approach to Background/ Foreground Separation for Moving Object Detection\u0026rdquo;, Neural Computing and Applications19(2):179-186, (2010) - PDF - BibTeX\nInteractive data analysis and clustering of genomic data, A. Ciaramella, S. Cocozza, F. Iorio,G. Miele, F. Napolitano, M. Pinelli, G. Raiconi, R. Tagliaferri, Neural Networks, vol. 21,Issues 2-3, pp. 368-378, ISSN: 0893-6080, doi: 10.1016/ j.neunet.2007.12.026, 2008;\nClustering and visualization approaches for human cell cycle gene expression data analysis, F. Napolitano, G. Raiconi, R. Tagliaferri, A. Ciaramella, A. Staiano, G. Miele,InternationalJournal of Approximate Reasoning, vol. 47, Issue 1, pp. 70-84, ISSN: 0888-613,doi:10.1016/j.ijar.2007.03.013, 2008;\nNeural Network Techniques for Proactive Password Checking, A. Ciaramella, P. D’Arco, A.De Santis, C. Galdi, R. Tagliaferri,IEEE Transactions on Dependable and SecureComputing, Volume 3, Issue 4, Oct.-Dec. 2006 Page(s):327 - 339, ISSN: 1545-5971,doi:10.1109/TDSC.2006.53, 2006;\nA Multi-Step Approach to Time Series Analysis and Gene Expression Clustering, R. Am-ato, A. Ciaramella, N. Deniskina, C. Del Mondo, D. di Bernardo, C. Donalek, G. Longo, G.Mangano, G. Miele, G. Raiconi, A. Staiano, R. Tagliaferri,Bioinformatics, vol. 22, n. 5, pp.589-596, ISSN: 1367-4803, doi: 10.1093/bioinformatics/btk026, 2006;\nFuzzy Relational Neural Network, A. Ciaramella, R. Tagliaferri, W. Pedrycz, A. Di Nola,International Journal of Approximate Reasoning, vol. 41, pp. 146-163, ISSN: 0888-613, doi:10.1016/j.ijar.2005.06.016, 2006;\nICA Based Identification of Dynamical Systems Generating Synthetic and Real World TimeSeries, A. Ciaramella, E. De Lauro, S. De Martino, M. Falanga, R. Tagliaferri,Soft Com-puting, vol. 10, pp. 587-606, ISSN: 1432-7643, doi: 10.1007/s00500-005-0515-7, 2006;\nSeparation of Convolved Mixtures in Frequency Domain ICA, A. Ciaramella, M. Funaro,R. Tagliaferri,International Mathematical Forum, vol. 1, no. 16, pp. 769-795, ISSN:1312-7594, doi: 10.12988/imf, 2006;\nComplexity of Time Series Associated to Dynamical Systems Inferred from IndependentComponent Analysis, A. Ciaramella, E. de Lauro, S. De Martino, M. Falanga, R. Tagli-aferri,Physical Review E., 72, 046712-1/14, ISSN: 1539-3755, doi:10.1103/Phys-RevE.72.046712, 2005;\nNovel Techniques for Microarry Data Analysis, A. Ciaramella, R. Amato, A. Staiano, R.Tagliaferri, et al.,Journal of Theoretical and Computational Nanoscience, vol. 2, n.4, pp. 514-523, ISSN: 1546-1955, doi: http://dx.doi.org/ 10.1166/ jctn.2005.006, 2005;\nApplications of Neural Networks in Astronomy and Astroparticle Physics, A. Ciaramella,E. Donalek, A. Staiano, et al.,Recent Res. Devel. Astrophys., vol. 2, pp. 27-58,ISBN:9788177362954, 2005;\nThe Genetic Development of Ordinal Sums, A. Ciaramella, W. Pedrycz, R. Tagliaferri,FuzzySets and Systems, vol. 151, pp. 303-325, doi: 10.1016/j.fss.2004.07.003, ISSN: 0165-0114, 2005;\nCharacterization of Strombolian Events by Using Independent Component Analysis, A. Cia-ramella, E. De Lauro, S. De Martino, B. Di Lieto, M. Falanga, R. Tagliaferri,NonlinearProcesses in Geophysics, vol. 11, pp. 453-461, ISSN: 1023-5809, 2004;\nA Multifrequency Analysis of Radio Variability of Blazars, A. Ciaramella, C. Bongardo, H.D. Aller, M. F. Aller, G. De Zotti, A. Lähteenmaki, G. Longo, L. Milano, R. Tagliaferri, H.Teräsranta, M. Tornikoski, S. Urpo,Astronomy \u0026amp; Astrophysics Journal, vol. 419, pp.485-500, ISSN: 0004-6361, doi:10.1051/0004-6361:20035771, 2004;\nPolarisation analysis of the independent components of low frequency events at Strombolivolcano (Eolian Islands, Italy), F. Acernese , A. Ciaramella, S. De Martino, M. Falanga, C.Godano, R. Tagliaferri,Journal of Volcanology and Geothermal Research, ElsevierJournals, n. 137, pp. 153-168, ISSN: 0377-0273, doi:10.1016/j.jvolgeores.2004.05.005,2004;\nNeural Networks in Astronomy, R. Tagliaferri, G. Longo, L. Milano, F. Acernese, F. Barone,A. Ciaramella, R. De Rosa, C. Donalek, A. Eleuteri, G. Raiconi, S. Sessa, A. Staiano, A.Volpicelli,Neural Networks, vol. 16, N. 3-4, pp. 295-319, 2003, ISSN: 0893-6080,doi:10.1016/S0893-6080(03)00028-5, 2003;\nNeural Networks for Blind-Source Separation of Stromboli Explosion Quakes, F. Acernese,A. Ciaramella, S. De Martino, R. De Rosa, M. Falanga, R. Tagliaferri,IEEE Transac-tions on Neural Networks, vol. 14, Issue: 1, pp. 167-175, ISSN: 1045-9227,doi:10.1109/TNN.2002.806649, 2003;\nSoft Computing Methodologies for Spectral Analysis in Cyclostratigraphy, R. Tagliaferri, N.Pelosi, A. Ciaramella, G. Longo, M. Milano, F. Barone,Computers and Geosciences, vol.27, issue 5, pp. 535-548, ISSN: 0098-3004, doi: 10.1016/S0098-3004(00)00166-7, 2001;\nSpectral Analysis of Stellar Light Curves by Means of Neural Networks, R. Tagliaferri, A.Ciaramella, L.Milano, F. Barone, G. Longo,Astronomy and Astrophysics SupplementSeries, vol. 137, pp. 391-405, ISSN: 0004-6361, 1999;\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"cf8994d53ae8ba3b31bb863b6979fd61","permalink":"/publications/journal/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/publications/journal/","section":"publications","summary":"Antonio Maratea, Angelo Ciaramella, Giuseppe Pio Cianci, Record linkage of banks and municipalities through multiple criteria and neural networks, PeerJ Computer Science 6:e258 https://doi.org/10.7717/peerj-cs.258, 2020\nDavide Nardone, Andelo Ciaramella, Antonino Staiano, A Sparse-Modeling Based Approach for Class Specific Feature Selection, PeerJ Computer Science, 5:e237, doi.org/10.7717/peerj-cs.237, 2019\nAngelo Ciaramella, Antonino Staiano, On the Role of Clustering and Visualization Techniques in Gene Microarray Data, A. Ciaramella, A. Staiano, Algorithms, 12(6), 123, 2019","tags":null,"title":"Journal Papers","type":"publications"},{"authors":null,"categories":null,"content":"PhD of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area: Computer Vision and Pattern Recognition   Research Interests: Content Based Image Retrieval System, Graph Matching, First Person Vision   E-mail: mario.manzo@uniparthenope.it   Homepage: http://www.dsi.unimi.it/persona.php?z=0\u0026amp;id=594   Phone: +39 0815476656   Fax: +39 0815476614   Room: CVPRlab - 4th Floor, North    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"98a36b7e3ad5791a086070de83c8b80b","permalink":"/staff/phdstudents/manzo/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/phdstudents/manzo/","section":"staff","summary":"PhD of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area: Computer Vision and Pattern Recognition   Research Interests: Content Based Image Retrieval System, Graph Matching, First Person Vision   E-mail: mario.manzo@uniparthenope.it   Homepage: http://www.dsi.unimi.it/persona.php?z=0\u0026amp;id=594   Phone: +39 0815476656   Fax: +39 0815476614   Room: CVPRlab - 4th Floor, North    ","tags":null,"title":"Mario Manzo","type":"staff"},{"authors":null,"categories":null,"content":"Post-Doc of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         **CVPR Research Area:**    Research Interests:    E-mail:    Phone:    Fax:    Room:     ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"f1159796704adad291ef88af7dffed1a","permalink":"/staff/alumni/dicapua/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/alumni/dicapua/","section":"staff","summary":"Post-Doc of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         **CVPR Research Area:**    Research Interests:    E-mail:    Phone:    Fax:    Room:     ","tags":null,"title":"Michele Di Capua","type":"staff"},{"authors":null,"categories":null,"content":"Sequence MSA\nSequence MSA is a home-made indoor sequence manually labeled, consisting of 528 frames of 320*240 spatial resolution, acquired at a frequency of 30 fps. The scene consists of a university hall, where a man comes in, leaves a bag on the floor, and then comes out. It represents an example of easy sequence, in that lighting conditions are quite stable and moving objects are well contrasted with the background (there is no camouflage); however, strong shadows cast by moving objects can be observed in the entire sequence.\nSome snapshots:\n          Download the sequence here (27.2MB rar file containing 528 PPM images).    Download the Ground Truth here (txt file containing, for each sequence frame, number of moving objects and their bounding box coordinates).    \\\n   Please cite:     L. Maddalena, A. Petrosino, A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications, IEEE Transactions on Image Processing, vol. 17, no. 7, 1168-1177, 2008.    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"9ebe88c2db2634b37b75d7ea17084bd1","permalink":"/code/modse/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/code/modse/","section":"code","summary":"Sequence MSA\nSequence MSA is a home-made indoor sequence manually labeled, consisting of 528 frames of 320*240 spatial resolution, acquired at a frequency of 30 fps. The scene consists of a university hall, where a man comes in, leaves a bag on the floor, and then comes out. It represents an example of easy sequence, in that lighting conditions are quite stable and moving objects are well contrasted with the background (there is no camouflage); however, strong shadows cast by moving objects can be observed in the entire sequence.","tags":null,"title":"Moving Object Detection Sequences","type":"code"},{"authors":null,"categories":null,"content":"Implementation of SOBS algorithm as described in:\nL. Maddalena, A. Petrosino, A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications, IEEE Transactions on Image Processing, DOI: 10.1109/TIP.2008.924285, Vol. 17, no. 7, pagg. 1168-1177, July 2008\nUsage:\nwhere\n \u0026lt;SeqName\u0026gt;: sequence name (complete path), not including frame numbers. Image sequences consist of binary PPM image frames with consecutive numbers, named in the following form  The number of digits for must be the same for all sequence frames (e.g. a sequence with 120 frames must be numbered from 1001 to 1120, and not from 1 to 120)\n  \u0026lt;#FirstFrame\u0026gt;, \u0026lt;#LastFrame\u0026gt;: number of first and last sequence frame to be considered.\n  [parameters]: optional, including:\n  -n #: (square root of) number of weight vectors for each pixel. Default 3\n  -K #: Number of initial frames for calibration. Default 200\n  -e1 #: Distance threshold e1 for calibration phase (eqn. (2)). Default 0.1\n  -e2 #: Distance threshold e2 for online phase (eqn. (2)). Default 0.03\n  -c1 #: Learning rate c1 for calibration phase (eqn. (4)). Default 1.0\n  -c2 #: Learning rate c2 for online phase (eqn. (4)). Default 0.05\n  -g #: Value for g in eqn. (5). Default 0.7\n  -b #: Value for b in eqn. (5). Default 1.0\n  -tS #: Value for tS in eqn. (5). Default 0.1\n  -tH #: Value for tH in eqn. (5). Default 10.0\n  -s: To apply shadow removal. Default: no shadow removal\n  -m: To save background model images. Default: do not save\n  -l: To save just last detection mask. Default: save all\n  \u0026lt;!--[if !supportEmptyParas]--\u0026gt; \u0026lt;!--[endif]--\u0026gt;\n    Example of use:\nwhere sequence WavingTrees, coming from sequences adopted in K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: principles and practice of background maintenance,” in Proc. 7th IEEE Conf. Computer Vision, 1999, vol. 1, pp. 255–261, has been saved in binary PPM image files named:\nand stored in directory c:/Sequences/WavingTrees. \\\n          Download the SOBS software here.    ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"6a4a096bf5fd61363020072610bac064","permalink":"/code/mods/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/code/mods/","section":"code","summary":"Implementation of SOBS algorithm as described in:\nL. Maddalena, A. Petrosino, A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications, IEEE Transactions on Image Processing, DOI: 10.1109/TIP.2008.924285, Vol. 17, no. 7, pagg. 1168-1177, July 2008\nUsage:\nwhere\n \u0026lt;SeqName\u0026gt;: sequence name (complete path), not including frame numbers. Image sequences consist of binary PPM image frames with consecutive numbers, named in the following form  The number of digits for must be the same for all sequence frames (e.","tags":null,"title":"Moving Object Detection Software","type":"code"},{"authors":null,"categories":null,"content":"This page has been created in order to distribute a prototype software implementing the Spatially Coherent Self-Organizing Background Subtraction (SC-SOBS) algorithm presented in\nL. Maddalena, A. Petrosino, The SOBS Algorithm: What Are the Limits?, IEEE Workshop on Change Detection, at CVPR 2012\nIf you use the software, please cite the above mentioned paper.\nClick here to download the Windows executable (WinZip compressed) together with the needed OpenCV .dll's. Basic usage for Change Detection Competition: Unzip the SC-SOBS.exe and the .dll's into the directory holding the \u0026ldquo;dataset\u0026rdquo; directory containing the Change Detection Challenge sequences [1]. To obtain all masks for the \u0026ldquo;baseline/highway\u0026rdquo; sequence, just type:\nSC-SOBS dataset/baseline/highway/input/in\nClick here to download the masks computed by SC-SOBS for the whole dataset. Usage with generic image sequences:\nSC-SOBS \u0026lt;SeqName\u0026gt; \u0026lt;#FirstFrame\u0026gt; \u0026lt;#LastFrame\u0026gt; [Parameters]\nwhere\n  \u0026lt;SeqName\u0026gt;: sequence name (complete path), not including frame numbers. Image sequences consist of binary .jpg image frames with consecutive 6 digit numbers, named in the following form \u0026lt;SeqName\u0026gt;\u0026lt;number\u0026gt;.jpg\n  \u0026lt;#FirstFrame\u0026gt;: number of first sequence frame to be considered. Default: 1\n  \u0026lt;#LastFrame\u0026gt;: number of last sequence frame to be considered. Default: toIdx (toIdx read from file ‘temporalROI.txt’ as in [1])\n  [parameters]: optional, including:\n  -n #: (square root of) number of weight vectors for each pixel. Default: 3\n  -K #: Number of initial frames for training. Default: fromIdx-1 (fromIdx read from file ‘temporalROI.txt’ as in [1])\n  -e1 #: Distance threshold e1 for training phase (Eq. (12)). Default: 1.0\n  -e2 #: Distance threshold e2 for testing phase (Eq. (2)). Default: 0.008\n  -c1 #: Learning rate c1 for training phase (Eq. (14)). Default: 1.0\n  -c2 #: Learning rate c2 for testing phase (Eq. (14)). Default: 0.05\n  -Cw #: Size of the neighbourhood for Spatial Coherence (Eq. (10)). Default: 5\n  -s #: To apply shadow removal (as in [2]). Default: 1 (apply)\n  -g #: Shadow detection value for g in eqn. (5) in [2]. Default: 0.7\n  -b #: Shadow detection value for b in eqn. (5) in [2]. Default: 1.0\n  -tS #: Shadow detection value for tS in eqn. (5) in [2]. Default: 0.1\n  -tH #: Shadow detection value for tH in eqn. (5) in [2]. Default: 10.0\n  -ROI #: To use ROI.bmp mask as in [1]. Default: 1 (do use)\n  -Med #: Size of the neighbourhood for Median Filtering Post-Processing. Default: 0 (no Post-Processing)\n  -m #: To save background model images. Default: 0 (do not save; only models for frames K-1 and #LastFrame are saved)\n  -l #: To save only last detection mask. Default 0 (save all in the temporal ROI)\n    Examples of use with generic image sequences:\n SC-SOBS  Provides the above information on usage.\nSC-SOBS c:/Sequences/WavingTrees/WavingTrees 1000 1247 -K 200 -e1 0.1 -e2 0.03 -c1 1.0 -c2 0.05 –l 1 –ROI 0  where sequence WavingTrees, coming from sequences adopted in K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: principles and practice of background maintenance,” in Proc. 7th IEEE Conf. Computer Vision, 1999, vol. 1, pp. 255–261, has been saved in binary .jpg image files named:\nWavingTrees001000.jpg, …, WavingTrees001247.jpg\nand stored in directory c:/Sequences/WavingTrees. This gives the moving object detection mask for last frame (named bin001247.png) as well the background model (named Model001199.ppm) achieved by training on the first 200 frames and the updated background model (named Model001247.ppm) for the last frame.\n3) SC-SOBS c:/Sequences/WavingTrees/WavingTrees 1000 1247 -K 200 -e1 0.1 -e2 0.03 -c1 1.0 -c2 0.05 –l 1 –ROI 0 –Med 3\nsame as before, but applying median filtering post-processing in a 3x3 neighbourhood to masks (through OpenCV function cvSmooth).\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"63f3ab074f4d4bdd06ce80b684629a48","permalink":"/code/sc-sobs/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/code/sc-sobs/","section":"code","summary":"This page has been created in order to distribute a prototype software implementing the Spatially Coherent Self-Organizing Background Subtraction (SC-SOBS) algorithm presented in\nL. Maddalena, A. Petrosino, The SOBS Algorithm: What Are the Limits?, IEEE Workshop on Change Detection, at CVPR 2012\nIf you use the software, please cite the above mentioned paper.\nClick here to download the Windows executable (WinZip compressed) together with the needed OpenCV .dll's. Basic usage for Change Detection Competition: Unzip the SC-SOBS.","tags":null,"title":"Moving Object Detection Software: SC-SOBS","type":"code"},{"authors":null,"categories":null,"content":"          Emanuele Di Nardo     ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"c275b9b0dba0d16ac7ee885b2314125e","permalink":"/staff/phdstudents/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/phdstudents/","section":"staff","summary":"          Emanuele Di Nardo     ","tags":null,"title":"PhD Students","type":"staff"},{"authors":null,"categories":null,"content":"Researcher of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area:    Research Interests:    E-mail: reaffaele.montella@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=49   Phone: +39 0815476613   Fax: +39 0815476514   Room:     ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"ed94b1b6fcc597daa7fe5aa888a70d64","permalink":"/staff/internalstaff/montella/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/internalstaff/montella/","section":"staff","summary":"Researcher of Computer ScienceDepartment of Applied Science, University of Naples Parthenope         CVPR Research Area:    Research Interests:    E-mail: reaffaele.montella@uniparthenope.it   Homepage: http://dist.uniparthenope.it/docenti_pagina.php?id_nomina=49   Phone: +39 0815476613   Fax: +39 0815476514   Room:     ","tags":null,"title":"Raffaele Montella","type":"staff"},{"authors":null,"categories":null,"content":"ROSE is a machine learning algorithm that implements outlier detection of an unlabeled spatiotemporal dataset using a rough set approach. It also provides a representative subset of the original data, describing the same structure, with which it is possible to detect the same outliers, named kernel set.\nThe paper that reports all the details and should be cited when the code is used is\nAlbanese A, Sankar K P, Petrosino A., IEEE Transactions on Knowledge and Data Engineering, Vol. 26, no. 1, pp. 194-207, 2014, DOI: 10.1109/TKDE.2012.234.\nROSE is written in Java and can be downloaded here. It is platform independent and bundled as jar package. Java Runtime Environment (JRE) is needed to run this software.\nROSE package content:  ROSE.jar (java executable file) README.md LICENSE sample_dataset.txt Original paper 2014_Albanese_Sankar_Petrosino.pdf Supplementary material folder ttk2014010194  ROSE can be executed from a Unix-like shell, a Terminal for Mac-OS or Windows CMD as follows:\njava -jar ROSE.jar \u0026lt;path input dataset\u0026gt; \u0026lt;number of outliers\u0026gt; \u0026lt;number of neighbors\u0026gt; \u0026lt;number of elements once\u0026gt; \u0026lt;alpha\u0026gt;\nThis is an example using the bundled sample dataset (assuming you are in the ROSE root folder):\njava -jar ROSE.jar sample_dataset.txt 16 16 18 1\nInput parameters  path input dataset - Input dataset filename (complete absolute path). This has to be a text file where each row contains numerical values, ranging in [0, 1] (normalized). Each numeric value must be space separated (two or three space characters are allowed). These values are the coordinates of the instances to be processed.\nEach row specifies:   required configuration: (x,y,t) triplets where (x,y) is a spatial coordinate and (t) is a timestamp.\nExample:\n0.84506904 0.10248589 0.15307587\n0.84543708 0.10339779 0.15308264\n0.84524589 0.10418321 0.15308941\n0.84540431 0.10549813 0.15309618\n0.84588570 0.10667086 0.15310294\\\n  optional configuration: 13 values are allowed at most, (x,y,t) + (f1,\u0026hellip;,f10) where the first 3 values are inherited from required configuration and (f1,\u0026hellip;,f10) is a list of other features.\nExample:\n0.84506904 0.10248589 0.15307587 0.84588570 0.10667086 0.15310294\n0.84543708 0.10339779 0.15308264 0.84524589 0.10418321 0.15308941\n0.84540431 0.10549813 0.15309618 0.84588570 0.10667086 0.15310294\\\n  standard configuration: not all 13 values need to be specified and just some features are specified (the sample dataset only contains (x,y,t)).\n   number of outliers - the number of outliers to be extracted from the data set. number of neighbors - the number of neighbors to be considered. number of elements once - it is the number of items that are extracted from the input data set at each iteration (and then compared with the rest of the file). This must be greater than the number of outliers. number of elements once \u0026gt; number of outliers alpha - is a multiplier value of the linear combination of the weights, ranging between [0,1] (for spatial weight) and 1 - alpha (for temporal weight). Special cases: alpha = 1 only spatial components are considered. alpha = 0 only temporal component is considered.  Output data Each execution of the ROSE software will produce an output folder named as the dataset input file name. Each execution on the same input dataset will overwrite the existing output folder.\n TopOutlier-numoutlier-alpha-weight-milliseconds.txt - (milliseconds value in the filename is used to maintain processing history, TopOutlier-inputfilename-numoutliers-alpha-weight.txt is the most recent iteration). These files contain TopOutliers search results at each iteration. The last two files contain the Lower and Upper approximation of the outlier set respectively. WeightedData-inputfilename-numoutliers-weight.txt - contains the starting points set with weights for each point. SolvingSet-milliseconds.txt - contains the kernel set (milliseconds value in the filename is used to maintain processing history, SolvingSet.txt is the most recent iteration). NegativeRegion.txt - contains discarded instances. \\   ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"b815374ccab90ac15ddaa868c89371c8","permalink":"/code/rose/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/code/rose/","section":"code","summary":"ROSE is a machine learning algorithm that implements outlier detection of an unlabeled spatiotemporal dataset using a rough set approach. It also provides a representative subset of the original data, describing the same structure, with which it is possible to detect the same outliers, named kernel set.\nThe paper that reports all the details and should be cited when the code is used is\nAlbanese A, Sankar K P, Petrosino A.","tags":null,"title":"ROSE (Rough Outlier Set Extraction)","type":"code"},{"authors":null,"categories":null,"content":"undergraduate students  Bevilacqua Vincenzo -\u0026gt; Signal Processing (INGV) Di Marino Antonio -\u0026gt; Signal Processing Lombardi Andrea -\u0026gt; Computational Biology Peluso Marika -\u0026gt; Computational Biology Silvio Vincenzo -\u0026gt; Computational Biology  Masters degree students  Abate Antonio -\u0026gt; Signal Processing (INGV) Bennato Maria Laura -\u0026gt; Computational Intelligence Methodologies De Falco Antonio -\u0026gt; -\u0026gt; Computational Biology Scandurra Andrea -\u0026gt; Signal Processing  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"813132384698d39e90783ad6fa3943d7","permalink":"/staff/students/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/staff/students/","section":"staff","summary":"undergraduate students  Bevilacqua Vincenzo -\u0026gt; Signal Processing (INGV) Di Marino Antonio -\u0026gt; Signal Processing Lombardi Andrea -\u0026gt; Computational Biology Peluso Marika -\u0026gt; Computational Biology Silvio Vincenzo -\u0026gt; Computational Biology  Masters degree students  Abate Antonio -\u0026gt; Signal Processing (INGV) Bennato Maria Laura -\u0026gt; Computational Intelligence Methodologies De Falco Antonio -\u0026gt; -\u0026gt; Computational Biology Scandurra Andrea -\u0026gt; Signal Processing  ","tags":null,"title":"Students","type":"staff"}]