Publications

@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}

}

@inproceedings{brancati_robust_2015,

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},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7169798},

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}

}