@ARTICLE{Coscia3DHP,
author={Coscia, P. and Palmieri, F.A.N. and Castaldo, F. and Cavallo, A.},
title={3-d hand pose estimation from kinect’s point cloud using appearance matching},
journal={Smart Innovation, Systems and Technologies},
year={2016},
volume={54},
pages={37-45},
doi={10.1007/978-3-319-33747-0_4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977159850&doi=10.1007%2f978-3-319-33747-0_4&partnerID=40&md5=a60c390db58dc10ea00bfba1eaf6e3ef},
affiliation={Seconda Universitá di Napoli (SUN), Dipartimento di Ingegneria Industriale e dell’Informazione, via Roma 29, Aversa, CE  81030, Italy},
abstract={We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to the synthetic models. The proposed framework uses a “pure” point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. © Springer International Publishing Switzerland 2016.},
editor={Esposito A., Esposito A., Morabito F.C., Pasero E., Bassis S.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={21903018},
isbn={9783319337463},
}