@conference {Iacopino2008364,
	title = {Artificial neural networks based symbolic gesture interface},
	booktitle = {SIGMAP 2008 - Proceedings of the International Conference on Signal Processing and Multimedia Applications},
	year = {2008},
	note = {cited By 0},
	pages = {364-369},
	abstract = {The purpose of the developed system is the realization of a gesture recognizer, applied to a user interface. We tried to get fast and easy software for user, without leaving out reliability and using instruments available to common user: a PC and a webcam. The gesture detection is based on well-known artificial vision techniques, as the tracking algorithm by Lucas and Kanade. The paths, opportunely selected, are recognized by a double layered architecture of multilayer perceptrons. The realized system is efficiency and has a good robustness, paying attention to an adequate learning of gesture vocabulary both for the user and for system.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-55849128216\&partnerID=40\&md5=ddfd2afde9d99702ec8cad5b4c8a5656},
	author = {Iacopino, C. and A. Montesanto and Paola Baldassarri and Dragoni, A.F. and Paolo Puliti}
}
@conference {Montesanto2008356,
	title = {Capturing the human action semantics using a query-by-example},
	booktitle = {SIGMAP 2008 - Proceedings of the International Conference on Signal Processing and Multimedia Applications},
	year = {2008},
	note = {cited By 0},
	pages = {356-363},
	abstract = {The paper describes a method for extracting human action semantics in video{\textquoteright}s using queries-by-example.b Here we consider the indexing and the matching problems of content-based human motion data retrieval. The query formulation is based on trajectories that may be easily built or extracted by following relevant points on a video, by a novice user too. The so realized trajectories contain high value of action semantics. The semantic schema is built by splitting a trajectory in time ordered sub-sequences that contain the features of extracted points. This kind of semantic representation allows reducing the search space dimensionality and, being human-oriented, allows a selective recognition of actions that are very similar among them. A neural network system analyzes the video semantic similarity, using a two-layer architecture of multilayer perceptrons, which is able to learn the semantic schema of the actions and to recognize them.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-55849129652\&partnerID=40\&md5=a8587b1b9833a9ccf64510cc75e426ab},
	author = {A. Montesanto and Paola Baldassarri and Dragoni, A.F. and Vallesi, G. and Paolo Puliti}
}
@article {Baldassarri2007367,
	title = {Detecting anomalous traffic using statistical discriminator and neural decisional motor},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {4527 LNCS},
	number = {PART 1},
	year = {2007},
	note = {cited By 0},
	pages = {367-376},
	abstract = {One of the main challenges in the information security concerns the introduction of systems able to identify intrusions. In this ambit this work takes place describing a new Intrusion Detection System based on anomaly approach. We realized a system with a hybrid solution between host-based and network-based approaches, and it consisted of two subsystems: a statistical system and a neural one. The features extracted from the network traffic belong only to the IP Header and their trend allows us detecting through a simple visual inspection if an attack occurred. Really the two-tier neural system has to indicate the status of the system. It classifies the traffic of the monitored host, distinguishing the background traffic from the anomalous one. Besides, a very important aspect is that the system is able to classify different instances of the same attack in the same class, establishing which attack occurs. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-38149012228\&partnerID=40\&md5=a6b1888f705a83520f222e21f9d88e66},
	author = {Paola Baldassarri and A. Montesanto and Paolo Puliti}
}
@conference {Baldassarri200774,
	title = {Detecting anomalous traffic using statistical processing and self-organizing maps},
	booktitle = {SECRYPT 2007 - International Conference on Security and Cryptography, Proceedings},
	year = {2007},
	note = {cited By 0},
	pages = {74-79},
	abstract = {The main idea of the present work is to create a system able to detect intrusions in computer networks. For this purpose we propose a novel intrusion detection system (IDS) based on an anomaly approach. We analyzed the network traffic from (outbound traffic) and towards (inbound traffic) a victim host through another host. Besides we realized an architecture consisted of two subsystems: a statistical subsystem and a neural networks based subsystem. The first elaborates chosen features extracted from the network traffic and it allows determining if an attack occurs through a preliminary visual inspection. The neural subsystem receives in input the output of the statistical subsystem and it has to indicate the status of the monitored host. It classifies the network traffic distinguishing the background traffic from the anomalous one. Moreover the system has to be able to classify different instances of the same attack in the same class, distinguishing in a completely autonomous way different typology of attack.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-67649764419\&partnerID=40\&md5=9a732b07f49d01bbb12964089390b414},
	author = {Paola Baldassarri and A. Montesanto and Paolo Puliti}
}
@conference {Montesanto2007229,
	title = {Fingerprints recognition using minutiae extraction: A fuzzy approach},
	booktitle = {Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007},
	year = {2007},
	note = {cited By 0},
	pages = {229-234},
	abstract = {The aim of this paper is to study the fingerprint verification based on local ridge discontinuities features (minutiae) only using grey scale images. We extract minutiae using two algorithms those following ridge lines and then recording ridge endings and bifurcations. Moreover we use a third algorithm able to develop a minutiae verification processing a local area using a neural network ( multilayer perceptron). Fingerprint distortion is filtered using a minutiae whole representation based on regular invariant moments. The results of the three minutiae extraction algorithms are joined during the minutiae pattern matching phase for fingerprint verification. Here we propose a new method of matching that use fuzzy operator to bypass the problem of different numbers of minutiae extracted from the algorithms. Experimental evidences show fingerprint recognition up to 95\%. {\textcopyright} 2007 IEEE.},
	doi = {10.1109/ICIAP.2007.4362784},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-48149113403\&partnerID=40\&md5=01f07886247083f3db66c7477c334984},
	author = {A. Montesanto and Paola Baldassarri and Vallesi, G. and Guido Tascini}
}
@article {Montesanto200684,
	title = {Navigation with memory in a partially observable environment},
	journal = {Robotics and Autonomous Systems},
	volume = {54},
	number = {1},
	year = {2006},
	note = {cited By 3},
	pages = {84-94},
	abstract = {The paper presents an architecture that allows the reactive visual navigation via an unsupervised reinforcement learning. This objective is reached using Q-learning and a hierarchical approach to the developed architecture. Using these techniques requires a deviation from the Partially Observable Markov Decision Processes (POMDP) and some innovations: heuristic techniques for generalizing the experience and for treating the partial observability; a technique for the speed adjournment of the Q function; the definition of a special reinforcement policy adequate for learning a complex task without supervision. The result is a satisfactory learning of the navigation assignment in a simulated environment. {\textcopyright} 2005 Elsevier B.V. All rights reserved.},
	doi = {10.1016/j.robot.2005.09.015},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-29344460057\&partnerID=40\&md5=a3f0d1229db6d5c8658cd509ca5acf81},
	author = {A. Montesanto and Guido Tascini and Paolo Puliti and Paola Baldassarri}
}
@article {Baldassarri2003201,
	title = {Self-organizing maps versus growing neural gas in a robotic application},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {2687},
	year = {2003},
	note = {cited By 1},
	pages = {201-208},
	abstract = {The paper proposes a method for visual based self-localisation of a mobile agent in indoor environment. The images acquired by the camera constitute an implicit topological representation of the environment. The environment is a priori unknown and so the implemented architecture is entirely unsupervised. To compare the performance of some self-organising neural networks, a similar neural network architecture of both Self-Organizing Map (SOM) and Growing Neural Gas (GNG) has been realized. Extensive simulations are provided to characterise the effectiveness of the GNG model in recognition speed, classification tasks and in particular topology preserving as compared to the SOM model. This behaviour depends on the following fact: a network (GNG) that adds nodes into map space can approximate the input space more accurately than a network with a predefined structure and size (SOM). The work shows that the GNG network is able to correctly reconstruct the environment topological map. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-35248829969\&partnerID=40\&md5=28fe2f7301103d80e28ff664c2a74906},
	author = {Paola Baldassarri and Paolo Puliti and A. Montesanto and Guido Tascini}
}
@conference {Baldassarri2003368,
	title = {Visual self-localisation using automatic topology construction},
	booktitle = {Proceedings - 12th International Conference on Image Analysis and Processing, ICIAP 2003},
	year = {2003},
	note = {cited By 0},
	pages = {368-374},
	abstract = {The paper proposes a machine learning method for self-localising a mobile agent, using the images supplied by a single omni-directional camera. The images acquired by the camera may be viewed as an implicit topological representation of the environment. The environment is a priori unknown and the topological representation is derived by unsupervised neural network architecture. The architecture includes a self-organising neural network, and is constituted by a growing neural gas, which is well known for its topology preserving quality. The growth depends on the topology that is not a priori defined, and on the need of discovering it, by the neural network, during the learning. The implemented system is able to recognise correctly the input frames and to reconstruct a topological map of the environment. Each node of the neural network identifies a single zone of the environment and the connections between the nodes correspond to the real space connections in the environment. {\textcopyright} 2003 IEEE.},
	doi = {10.1109/ICIAP.2003.1234077},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84890545853\&partnerID=40\&md5=6954a4c9b47cdb3e275eac96afdf95a2},
	author = {Paola Baldassarri and Paolo Puliti and A. Montesanto and Guido Tascini}
}
