@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}
}
@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}
}
@conference {Zingaretti1996129,
	title = {Imaging approach to real-time tracking of submarine pipeline},
	booktitle = {Proceedings of SPIE - The International Society for Optical Engineering},
	volume = {2661},
	year = {1996},
	note = {cited By 10},
	pages = {129-137},
	abstract = {The work presents a real-time underwater imaging system for identification and tracking of a submarine pipeline on a sequence of recorded images. The main novelty of this work relies on adopting an automatic approach that is entirely based on the analysis and interpretation of visual data, in spite of the various limitations upon the ability to image underwater objects. The analysis of the data is performed starting from image processing operations (like filtering, profile analysis, feature enhancement) implemented on a dedicated board. Then, the system employs an efficient dynamic process for recognizing the two contours of the pipeline. In each frame the system is able to determine the equations of the two straight lines corresponding to the pipeline contours. The system reaches satisfactory performances in real time operation: up to eight frames per second on a Pentium based PC. The results of this work are somewhat more meaningful as the input images were acquired by three cameras, mounted on a remotely operated vehicle travelling at one nautical mile an hour, without any attention either to illumination conditions or stability of cameras. This work is originated from the interest of Snamprogetti in enhancing the level of automation in submarine pipeline inspection.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0029754848\&partnerID=40\&md5=65001780f0cb94776ceeb1da3a6eec40},
	author = {Primo Zingaretti and Guido Tascini and Paolo Puliti and S.M. Zanoli}
}
@conference {Tascini1993126,
	title = {Handwritten character recognition using background analysis},
	booktitle = {Proceedings of SPIE - The International Society for Optical Engineering},
	volume = {1906},
	year = {1993},
	note = {cited By 0},
	pages = {126-133},
	abstract = {The paper describes a low-cost handwritten character recognizer. It is constituted by three modules: the {\textquoteleft}acquisition{\textquoteright} module, the {\textquoteleft}binarization{\textquoteright} module, and the {\textquoteleft}core{\textquoteright} module. The core module can be logically partitioned into six steps: character dilation, character circumscription, region and {\textquoteleft}profile{\textquoteright} analysis, {\textquoteleft}cut{\textquoteright} analysis, decision tree descent, and result validation. Firstly, it reduces the resolution of the binarized regions and detects the minimum rectangle (MR) which encloses the character; the MR partitions the background into regions that surround the character or are enclosed by it, and allows it to define features as {\textquoteleft}profiles{\textquoteright} and {\textquoteleft}cuts;{\textquoteright} a {\textquoteleft}profile{\textquoteright} is the set of vertical or horizontal minimum distances between a side of the MR and the character itself; a {\textquoteleft}cut{\textquoteright} is a vertical or horizontal image segment delimited by the MR. Then, the core module classifies the character by descending along the decision tree on the basis of the analysis of regions around the character, in particular of the {\textquoteleft}profiles{\textquoteright} and {\textquoteleft}cuts,{\textquoteright} and without using context information. Finally, it recognizes the character or reactivates the core module by analyzing validation test results. The recognizer is largely insensible to character discontinuity and is able to detect Arabic numerals and English alphabet capital letters. The recognition rate of a 32 {\texttimes} 32 pixel character is of about 97\% after the first iteration, and of over 98\% after the second iteration.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0027277522\&partnerID=40\&md5=06fd7da850b665db5896240114bab4e7},
	author = {Guido Tascini and Paolo Puliti and Primo Zingaretti}
}
@conference {Tascini1993322,
	title = {Retina vascular network recognition},
	booktitle = {Proceedings of SPIE - The International Society for Optical Engineering},
	volume = {1898},
	year = {1993},
	note = {cited By 5},
	pages = {322-329},
	abstract = {The analysis of morphological and structural modifications of the retina vascular network is an interesting investigation method in the study of diabetes and hypertension. Normally this analysis is carried out by qualitative evaluations, according to standardized criteria, though medical research attaches great importance to quantitative analysis of vessel color, shape and dimensions. The paper describes a system which automatically segments and recognizes the ocular fundus circulation and micro circulation network, and extracts a set of features related to morphometric aspects of vessels. For this class of images the classical segmentation methods seem weak. We propose a computer vision system in which segmentation and recognition phases are strictly connected. The system is hierarchically organized in four modules. Firstly the Image Enhancement Module (IEM) operates a set of custom image enhancements to remove blur and to prepare data for subsequent segmentation and recognition processes. Secondly the Papilla Border Analysis Module (PBAM) automatically recognizes number, position and local diameter of blood vessels departing from optical papilla. Then the Vessel Tracking Module (VTM) analyses vessels comparing the results of body and edge tracking and detects branches and crossings. Finally the Feature Extraction Module evaluates PBAM and VTM output data and extracts some numerical indexes. Used algorithms appear to be robust and have been successfully tested on various ocular fundus images.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0027873308\&partnerID=40\&md5=fe51e921b3d6ecbbbd0483a977269acd},
	author = {Guido Tascini and Passerini, G. and Paolo Puliti and Primo Zingaretti}
}
@conference {Tascini1991178,
	title = {Decision support system for capillaroscopic images},
	booktitle = {Proceedings of SPIE - The International Society for Optical Engineering},
	volume = {1450},
	year = {1991},
	note = {cited By 1},
	pages = {178-185},
	abstract = {The aim of the paper is to describe a decision support system operating in the area of capillaroscopic images. The system automatically sites the capillaroscopic analyzed image into one of the following classes: normal, diabetic and sclerodermic. The automatic morphometric analysis attempts to imitate physician behaviour and requires the introduction of some particular features connected with the specific domain. These features allow achieving a symbolic representation of the capillary partitioning it into three components: apex, arteriolar and venular side. The system is hierarchically organized in two levels. The system has been successfully used for obtaining images of nailfold capillaries of human finger.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0025798390\&partnerID=40\&md5=05b1aa9225d95f1ed2cecdc0a5268c2a},
	author = {Guido Tascini and Paolo Puliti and Primo Zingaretti}
}
@article {Palareti1988307,
	title = {Prolong approach to image segmentation},
	journal = {Applied Artificial Intelligence},
	volume = {2},
	number = {3-4},
	year = {1988},
	note = {cited By 0},
	pages = {307-331},
	abstract = {Segmentation is a problem in computer vision. It attempts to supply primitives for higher-level processes of interpretation. A multithreshold approach has already given acceptable results. This paper presents a Prolog implementation of a multithreshold system. The algorithms implemented concern image representation, connected regions, identification, and contour detection. The improvement of algorithms in a logic language rather than in a procedural one appeared to be of particular interest. The main reasons for this are implementation facility and the natural use of logic programming in the field of knowledge-based systems. In fact, a knowledge-based approach seems a reasonable solution to problems of assisted image understanding processes.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0024019798\&partnerID=40\&md5=bbf1d6d0aad82eee5540c0841df3bfad},
	author = {Palareti, Aldopaolo and Paolo Puliti and Guido Tascini and Primo Zingaretti}
}
