Combining depth information and color image, RGB-D cameras provide ready detection of humans and the associated 3D skeleton joints data, facilitating if not revolutionizing conventional image centric researches in, among others, computer vision, surveillance, and human activity analysis. Applicability of a D-RBG camera, however, is restricted by its limited range of frustum of depth in the range of 0.8 to 4 meters. Although a RGB-D camera network, constructed by deployment of several RGB-D cameras at various locations, could extend the range of coverage, it requires precise localization of the camera network: relative location and orientation of neighboring cameras. By introducing a skeleton-based viewpoint invariant transformation (SVIT) that derives relative location and orientation of a detected human's upper torso to a RGB-D camera, this paper presents a reliable automatic localization technique without the need for additional instrument or human intervention. By respectively applying SVIT to two neighboring RGB-D cameras on a commonly observed skeleton, the respective relative position and orientation of the detected human's skeleton to these two respective cameras can be obtained before being combined to yield the relative position and orientation of these two cameras, thus solving the localization problem. Experiments have been conducted where two Kinects are situated with bearing differences of about 45 degrees and 90 degrees when coverage extended by up to 70% with the installment of an additional Kinect. The same localization technique can be applied repeatedly to a larger number of RGB-D cameras, thus extending the applicability of RGB-D cameras to camera networks in making human behavior analysis and context-aware service in a lager surveillance area.

Summary: A web-based three-dimensional (3D) protein retrieval system is available for protein structure data including all PDB and FSSP dataset. In this system, we use a visual-based matching method to compare the protein structure from multiple viewpoints. It takes less than three seconds for each query with 90% accuracy on an average. Availability: The web-based query interface and downloadable files can be accessed via http://3d.csie.ntu.edu.tw/ProteinRetrieval/ Contact: [email protected] Supplementary information: Further details of the proposed method are available at http://graphics.csie.ntu.edu.tw/~jsyeh/3Dprotein/

We develop an interactive haptic system, which can be a further aid for digital Chinese painting. When an artist is holding our force feedback device, one feels like holding a real painting brush with all the contact and bending forces, since the viscosity, friction, and the bending force of a brush touching the paper are simulated. First we derive a physical dynamics model as bending springs for bristles to construct a 3D brush. Then we simulate the ink-water transfer system for ink spreading and color blending. Our system is a real-time system and users can interact with it holding a digital brush supported by either a Phantom force feedback device or a "WACOM pressure sensing pen" on a tablet. A pilot experiment was conducted, and the results show that brush writing with haptic feedback is better than that of the same visual display but without haptic feedback.

A robust, accurate and inexpensive approach to estimate 3D facial motion from multi-view video is proposed, where two mirrors located near one's cheeks can reflect the side views of markers on one face. Nice properties of mirrored images are utilized to simplify the proposed tracking algorithm significantly, while a Kalman filter is employed to reduce the noise and to predict the occluded marker positions. More than 50 markers on one face are continuously tracked at 30 frames per second. The estimated 3D facial motion data has been practically applied to our facial animation system. In addition, the dataset of facial motion can also be applied to the analysis of co-articulation effects, facial expressions, and audio-visual hybrid recognition system.