  Hand segmentation for hand-object interaction is a necessary preprocessing
step in many applications such as augmented reality, medical application, and
human-robot interaction. However, typical methods are based on color
information which is not robust to objects with skin color, skin pigment
difference, and light condition variations. Thus, we propose hand segmentation
method for hand-object interaction using only a depth map. It is challenging
because of the small depth difference between a hand and objects during an
interaction. To overcome this challenge, we propose the two-stage random
decision forest (RDF) method consisting of detecting hands and segmenting
hands. To validate the proposed method, we demonstrate results on the publicly
available dataset of hand segmentation for hand-object interaction. The
proposed method achieves high accuracy in short processing time comparing to
the other state-of-the-art methods.
