  In this paper, we present a method for simultaneous articulation model
estimation and segmentation of an articulated object in RGB-D images using
human hand motion. Our method uses the hand motion in the processes of the
initial articulation model estimation, ICP-based model parameter optimization,
and region selection of the target object. The hand motion gives an initial
guess of the articulation model: prismatic or revolute joint. The method
estimates the joint parameters by aligning the RGB-D images with the constraint
of the hand motion. Finally, the target regions are selected from the cluster
regions which move symmetrically along with the articulation model. Our
experimental results show the robustness of the proposed method for the various
objects.
