Publications

@inproceedings{augelloKinectBasedGestureAcquisition2020,

title = {A Kinect-Based Gesture Acquisition and Reproduction System for Humanoid Robots},

author = { Agnese Augello and Angelo Ciulla and Alfredo Cuzzocrea and Salvatore Gaglio and Giovanni Pilato and Filippo Vella},

editor = { O Gervasi and B Murgante and S Misra and C Garau and I Blecic and D Taniar and {relax BO} Apduhan and {relax AMAC} Rocha and E Tarantino and {relax CM} Torre and Y Karaca},

doi = {10.1007/978-3-030-58799-4_69},

isbn = {978-3-030-58799-4 978-3-030-58798-7},

year  = {2020},

date = {2020-01-01},

booktitle = {COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT I},

volume = {12249},

pages = {967--977},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Lecture Notes in Computer Science},

abstract = {The paper illustrates a system that endows an humanoid robot with the capability to mimic the motion of a human user in real time, serving as a basis for further gesture based human-robot interactions. The described approach uses the Microsoft Kinect as a low cost alternative to expensive motion capture devices.},

keywords = {Gestural Interaction, Humanoid Robots, RGB-D, Social Robots},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vellaClassificationIndoorActions2017,

title = {Classification of Indoor Actions through Deep Neural Networks},

author = { Filippo Vella and Agnese Augello and Umberto Maniscalco and Vincenzo Bentivenga and Salvatore Gaglio},

editor = { Dipanda A. Chbeir R. Gallo L. Yetongnon K. De Pietro G.},

doi = {10.1109/SITIS.2016.22},

isbn = {978-1-5090-5698-9},

year  = {2017},

date = {2017-01-01},

booktitle = {Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016},

pages = {82--87},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The raising number of elderly people urges the research of systems able to monitor and support people inside their domestic environment. An automatic system capturing data about the position of a person in the house, through accelerometers and RGBd cameras can monitor the person activities and produce outputs associating the movements to a given tasks or predicting the set of activities that will be executes. We considered, for the task the classification of the activities a Deep Convolutional Neural Network. We compared two different deep network and analyzed their outputs. textcopyright 2016 IEEE.},

keywords = {Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{brancatiRobustFingertipDetection2015,

title = {Robust Fingertip Detection in Egocentric Vision under Varying Illumination Conditions},

author = { Nadia Brancati and Giuseppe Caggianese and Maria Frucci and Luigi Gallo and Pietro Neroni},

doi = {10.1109/ICMEW.2015.7169798},

isbn = {978-1-4799-7079-7},

year  = {2015},

date = {2015-07-01},

urldate = {2016-12-06},

booktitle = {IEEE International Conference on Multimedia & Expo Workshops (ICMEW)},

pages = {1--6},

publisher = {IEEE},

address = {Torino, Italy},

abstract = {Wearable augmented reality (AR) systems have the potential to significantly lower the barriers to accessing information, while leaving the focus of the user's attention on the real world. To reveal their true potential, the human-machine interface is crucial. A touchless point-and-click interface for wearable AR systems may be suitable for use in many realworld applications, but it demands fingertip detection techniques robust enough to cope with cluttered backgrounds and varying illumination conditions. In this paper we propose an approach that, by automatically choosing between color and depth features, allows to detect the hand and then the user's fingertip both in indoor and outdoor scenarios, with or without adequate illumination.},

keywords = {Ego-Vision, Fingertip detection, Point-and-click interface, RGB-D, Touchless interaction},

pubstate = {published},

tppubtype = {inproceedings}

}