@article {,
	title = {Preterm infants{\textquoteright} pose estimation with spatio-temporal features},
	journal = {IEEE Transactions on Biomedical Engineering},
	year = {2019},
	doi = {10.1109/TBME.2019.2961448},
	url = {https://ieeexplore.ieee.org/abstract/document/8938758},
	author = {Sara Moccia and Lucia Migliorelli and Virgilio Carnielli and Emanuele Frontoni}
}
@book {,
	title = {Programmazione ad Oggetti per l{\textquoteright}Ingegneria Informatica},
	year = {2019},
	pages = {IX-412},
	publisher = {McGraw-Hill Education},
	organization = {McGraw-Hill Education},
	isbn = {9781307421903},
	issn = {1307421903},
	author = {Emanuele Frontoni and Adriano Mancini}
}
@article {Paolanti2018,
	title = {Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection},
	journal = {Sensors},
	volume = {18},
	number = {10},
	year = {2018},
	month = {oct},
	pages = {3471},
	publisher = {{MDPI} {AG}},
	doi = {10.3390/s18103471},
	url = {https://doi.org/10.3390/s18103471},
	author = {Marina Paolanti and Luca Romeo and Daniele Liciotti and Annalisa Cenci and Emanuele Frontoni and Primo Zingaretti}
}
@article {,
	title = {Prospective motor control obeys to idiosyncratic strategies in autism},
	journal = {Scientific reports},
	volume = {8},
	year = {2018},
	pages = {1{\textendash}9},
	author = {Cavallo, Andrea and Romeo, Luca and Ansuini, Caterina and Podda, Jessica and Battaglia, Francesca and Veneselli, Edvige and Pontil, Massimiliano and Becchio, Cristina}
}
@inbook {liciotti2017person,
	title = {Person Re-identification Dataset with RGB-D Camera in a Top-View Configuration},
	booktitle = {Video Analytics. Face and Facial Expression Recognition and Audience Measurement},
	volume = {1},
	year = {2017},
	author = {Daniele Liciotti and Marina Paolanti and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@inbook {liciotti2017pervasive,
	title = {Pervasive System for Consumer Behaviour Analysis in Retail Environments},
	booktitle = {Video Analytics. Face and Facial Expression Recognition and Audience Measurement},
	volume = {2},
	year = {2017},
	author = {Daniele Liciotti and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@conference {Mancini2014,
	title = {Point to point navigation for people with mobility impairments},
	booktitle = {MESA 2014 - 10th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Conference Proceedings},
	year = {2014},
	note = {cited By 0},
	abstract = {The wide availability of high resolution city maps has radically changed our life during the last years. In particular, the routing services offer the capability to plan a travel by taking into account different transportation modalities, e.g., by car, train, public transport, bike, foot. Actually these routing services are mainly oriented to the majority of end-users and does not take into account particular constraints. This paper introduces a novel framework to assist people with mobility impairments in point to point navigation. The proposed framework is based on the creation of a road graph with different levels of detail (LoD) that can be modified at run-time by users if an obstacle obstructs the pre-planned path. Sidewalks, crosswalks and dynamic obstacles are also considered during the planning due to their importance for the navigation of mobility-impaired users. The proposed framework is based on OpenStreetMap (OSM) to manage and model the road graph, OpenTripPlanner (OTP) to plan a dedicated path considering different constraints, and mobile platforms for real time navigation and obstacle detection.},
	doi = {10.1109/MESA.2014.6935622},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84911952948\&partnerID=40\&md5=a4464d155dc00c67ea2fbb6b0e214263},
	author = {Adriano Mancini and Primo Zingaretti}
}
@conference {Zingaretti2010185,
	title = {Particle clustering to improve omnidirectional localization in outdoor environments},
	booktitle = {Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2009, DETC2009},
	volume = {3},
	year = {2010},
	note = {cited By 1},
	pages = {185-192},
	abstract = {Monte Carlo Localization (MCL) is a common method for self-localization of a mobile robot under the assumption that a map of the environment is available. In addition to laser scan-ners and sonar sensors, localization approaches using vision sensors have also been recently developed with good results. In this paper we present two variations to improve the standard im-plementation of the MCL algorithm. The first change consists in a new strategy for the generation of particles, both at the initialization and at the resampling stage, which tries to generate new particles near the position of images in the learning dataset or in the neighborhood of particles with higher weights in the previous estimate, respectively. The second variation is related to a new approach to the estimate of the robot position, now based on two steps: clustering of particles and taking as estimate of robot position the center of the cluster, computed as a weighted sum of particle weights, with higher weight. The improved MCL algorithm described in this paper is compared with the standard MCL algorithm in terms of localization accuracy. In particular, tests were performed using local feature matching of omnidirectional images implemented on a real robot system operating in large outdoor environments with high dynamic content. Obtained results show that the localization accuracy of the improved MCL algorithm is more than twice that of the standard MCL algorithm. Copyright {\textcopyright} 2009 by ASME.},
	doi = {10.1115/DETC2009-87373},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953702059\&partnerID=40\&md5=bf8d639ae45538fb4d049602073a0907},
	author = {Primo Zingaretti and Ascani, A. and Adriano Mancini and Emanuele Frontoni}
}
@article {Khoshelham2010123,
	title = {Performance evaluation of automated approaches to building detection in multi-source aerial data},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
	volume = {65},
	number = {1},
	year = {2010},
	note = {cited By 25},
	pages = {123-133},
	abstract = {Automated approaches to building detection in multi-source aerial data are important in many applications, including map updating, city modeling, urban growth analysis and monitoring of informal settlements. This paper presents a comparative analysis of different methods for automated building detection in aerial images and laser data at different spatial resolutions. Five methods are tested in two study areas using features extracted at both pixel level and object level, but with the strong prerequisite of using the same training set for all methods. The evaluation of the methods is based on error measures obtained by superimposing the results on a manually generated reference map of each area. The results in both study areas show a better performance of the Dempster-Shafer and the AdaBoost methods, although these two methods also yield a number of unclassified pixels. The method of thresholding a normalized DSM performs well in terms of the detection rate and reliability in the less vegetated Mannheim study area, but also yields a high rate of false positive errors. The Bayesian methods perform better in the Memmingen study area where buildings have more or less the same heights. {\textcopyright} 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).},
	doi = {10.1016/j.isprsjprs.2009.09.005},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-72949110177\&partnerID=40\&md5=016e5e523686951f9fa75cccf7957c8d},
	author = {Khoshelham, K. and C. Nardinocchi and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@article {Bernardini201043,
	title = {Pixel, object and hybrid classification comparisons},
	journal = {Journal of Spatial Science},
	volume = {55},
	number = {1},
	year = {2010},
	note = {cited By 3},
	pages = {43-54},
	abstract = {The choice of the best classification approach for thematic map generation relies on many factors, such as image resolution and minimum mapping unit. The generalized GIS-ready products derived from the results of pixel-based approaches and the availability of higherresolution imagery have directed research towards object-based classification approaches. In this paper we present the superior performance of a hybrid methodology that combines the results of automatic segmentation with the land cover information derived from a pixel classification by means of the Winner Takes All (WTA) algorithm. Land use and land cover results obtained through this hybrid classification approach are compared with those of a One Against All (OAA) object-oriented classification approach. {\textcopyright} 2010 Surveying and Spatial Sciences Institute and Mapping Sciences Institute, Australia.},
	doi = {10.1080/14498596.2010.487641},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80052137947\&partnerID=40\&md5=74b7a4fe82987b5213c27b7315b0f768},
	author = {A. Bernardini and Emanuele Frontoni and Eva Savina Malinverni and Adriano Mancini and Anna Nora Tassetti and Primo Zingaretti}
}
@conference {Zingaretti2009185,
	title = {Particle clustering to improve omnidirectional localization in outdoor environments},
	booktitle = {Proceedings of the ASME Design Engineering Technical Conference},
	volume = {3},
	year = {2009},
	note = {cited By 0},
	pages = {185-192},
	abstract = {Monte Carlo Localization (MCL) is a common method for self-localization of a mobile robot under the assumption that a map of the environment is available. In addition to laser scanners and sonar sensors, localization approaches using vision sensors have also been recently developed with good results. In this paper we present two variations to improve the standard implementation of the MCL algorithm. The first change consists in a new strategy for the generation of particles, both at the initialization and at the resampling stage, which tries to generate new particles near the position of images in the learning dataset or in the neighborhood of particles with higher weights in the previous estimate, respectively. The second variation is related to a new approach to the estimate of the robot position, now based on two steps: clustering of particles and taking as estimate of robot position the center of the cluster, computed as a weighted sum of particle weights, with higher weight. The improved MCL algorithm described in this paper is compared with the standard MCL algorithm in terms of localization accuracy. In particular, tests were performed using local feature matching of omnidirectional images implemented on a real robot system operating in large outdoor environments with high dynamic content. Obtained results show that the localization accuracy of the improved MCL algorithm is more than twice that of the standard MCL algorithm. {\textcopyright} 2009 by ASME.},
	doi = {10.1115/DETC2009-87373},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-82155166811\&partnerID=40\&md5=46091a6bd1c837a8652000dffdfa0861},
	author = {Primo Zingaretti and Ascani, A. and Adriano Mancini and Emanuele Frontoni}
}
@conference {Frontoni2007,
	title = {Prototype UAV helicopter working in cooperative environments},
	booktitle = {IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM},
	year = {2007},
	note = {cited By 7},
	abstract = {Today small autonomous helicopters offer a low budget platform for aerial applications such as surveillance (both military and civilian), land management, and earth science. In this paper we introduce a prototype of autonomous aerial vehicle, the Helibot helicopter, specifically designed for applications in a cooperative network based on autonomous agents as UAV and UGV. We present our scalable and robust architecture, focusing in particular on hardware and real time solutions. A mechanical structure for safe testing is then presented. {\textcopyright}2007 IEEE.},
	doi = {10.1109/AIM.2007.4412564},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-48649092977\&partnerID=40\&md5=79d155f3eb244add87a4d8ba5d7616ed},
	author = {Emanuele Frontoni and Adriano Mancini and Caponetti, F. and Primo Zingaretti and Sauro Longhi}
}
@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}
}
