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.
3-D hand pose estimation from kinect’s point cloud using appearance matching / P. Coscia, F.A.N. Palmieri, F. Castaldo, A. Cavallo (SMART INNOVATION, SYSTEMS AND TECHNOLOGIES). - In: Advances in Neural Networks : Computational Intelligence for ICT / [a cura di] S. Bassis, A. Esposito, F.C. Morabito, E. Pasero. - [s.l] : Springer Science and Business Media Deutschland GmbH, 2016. - ISBN 978-3-319-33746-3. - pp. 37-45 (( convegno International Workshop on Neural Networks tenutosi a Vietri sul Mare nel 2015 [10.1007/978-3-319-33747-0_4].
3-D hand pose estimation from kinect’s point cloud using appearance matching
P. Coscia
Primo
;
2016
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.
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