Pasquale Coscia - 3-D Hand Pose Estimation from Kinect’s Point Cloud Using Appearance Matchin

3-D Hand Pose Estimation from Kinect’s Point Cloud Using Appearance Matching
Pasquale Coscia, Francesco A.N. Palmieri, Francesco Castaldo and Alberto Cavallo
25th International Workshop on Neural Networks (WIRN 2015)

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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3-D Hand Pose Estimation from Kinect’s Point Cloud Using Appearance MatchingPasquale Coscia, Francesco A.N. Palmieri, Francesco Castaldo and Alberto Cavallo25th International Workshop on Neural Networks (WIRN 2015)

3-D Hand Pose Estimation from Kinect’s Point Cloud Using Appearance Matching
Pasquale Coscia, Francesco A.N. Palmieri, Francesco Castaldo and Alberto Cavallo
25th International Workshop on Neural Networks (WIRN 2015)