3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching

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 hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. 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. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera

A hybrid method using kinect depth and color data stream for hand blobs segmentation

The recently developed depth sensors such as Kinect have provided new potential for human-computer interaction (HCI) and hand gesture are one of main parts in recent researches. Hand segmentation procedure is performed to acquire hand gesture from a captured image. In this paper, a method is produced to segment hand blobs using both depth and color data frames. This method applies a body segmentation and an image threshold techniques to depth data frame using skeleton data and concurrently it uses SLIC super-pixel segmentation method to extract hand blobs from color data frame with the help of skeleton data. The proposed method has low computation time and shows significant results when basic assumption are fulfilled