In this paper, we propose automatic hair detection and tracking system at video-rate by using Kinect to capture color and depth information. Our system has the following three ideas simultaneously: 1) Simple and high-speed system is built by the general technique using distance information. 2) Using a 6D feature vector to describe both the 3D color feature and 3D geometric feature of each pixel uniformly. Classifying pixels in images into foreground (e.g. hair) and background with K-means clustering algorithm. 3) Automatic learning and updating the cluster centers of foreground and background before and during hair tracking. This ability makes our system can track hairs robustly, which does not depend on its hair color and style, and even before a background with similar color of hair. Our system becomes a robust head tracking system if the face and hair are set as foreground.
Annotation is getting popular for Web documents. Especially, conditional annotation (annotation to be displayed depending on the user’s operations) is useful for users to use educational Web contents. In this paper, we propose W3annotator a system which enables users to make conditional annotations by programming by demonstration. Annotation creators input operation examples in W3Annotator. The system recommends operational events which might be useful for creating annotations. Just by selecting an operational event from the recommended ones and inputting messages, they can create conditional annotations. In the experiment, we asked professional creators to use our system. The results showed that time for inputting operations and messages is shorter than time for considering the content of annotations. They also showed that the users created annotations based on the recommended operational events.