We present a framework for articulated body model acquisition and tracking from voxel data. A 3D voxel reconstruction of the person's body is computed from silhouettes extracted from four cameras. The model acquisition process is fully automated. In the first frame, body parts are located sequentially. The head is located first, since its shape and size are unique and stable. Other parts are found by sequential template growing and fitting. This initial estimate of body part locations, sizes and orientations is then used as a measurement for the extended Kalman filter which ensures a valid articulated body model. The same filter, with a slightly modified state and state transition matrix, is then used for tracking. The performance of the system has been evaluated on several video sequences with promising results.

A novel approach for estimating 3D lip posture from monocular video se-quences is presented. The lips are modeled as a four body closed kinematic chain with each body possessing translational, rotational and prismatic (to ac-count for deformations) degrees of freedom. Geometric constraints relating these bodies to each other, and to the face as a whole, are used to constrain the space of possible lip postures recovered from each image. These constraints are used with the recently proposed Expectation Constrained Maximization algorithm to estimate the lip posture from video frames that have been pro-cessed (using a color segmentation algorithm described here) to identify lip regions. 1

Medical imaging is shifting from film to electronic images. The STARE (structured analysis of the retina) system is a sophisticated image management system that will automatically diagnose images, compare images, measure key features in images, annotate image contents, and search for images similar in content. We concentrate on automated diagnosis. The images are annotated by segmentation of objects of interest, classification of the extracted objects, and reasoning about the image contents. The inferencing is accomplished with Bayesian networks that learn from image examples of each disease. This effort at image understanding in fundus images anticipates the future use of medical images. As these capabilities mature, we expect that ophthalmologists and physicians in other fields that rely in images will use a system like STARE to reduce repetitive work, to provide assistance to physicians in difficult diagnoses or with unfamiliar diseases, and to manage images in large image databases. 1.

Human-machine interfaces play a role of growing importance as computer technology continues to evolve. Motivated by the desire to provide users with an intuitive gesture input system, we describe the design of a recursive filter applied to the vision-based gesture interpretation problem. The gestures are modeled as a hidden Markov model with the state representing the gesture sequences, and the observations being the current static hand pose. At each time step the recursive filter updates its estimate of what gesture is occurring based on the current extracted pose information. The result is a robust system which provides the user with continual feedback during compound gestures.>

The authors present a study of the performance achieved by user processes when using the IPC mechanisms as implemented in Berkeley Unix 4.2BSD in Ethernet based environments. The authors assess not only the impact that different processors, network hardware interfaces, and Ethernets have on the communication across machines, but also the effect of the loading of the hosts and communication media that participate in the interprocess communication mechanism. The measurements highlight the ultimate bounds on performance that may be achieved by user process applications communicating across machines, and serve as a guide in designing performance-critical applications. A detailed timing analysis is presented of the dynamic behavior of the TCP/IP and the UDP/IP network communication protocols' implementation in Berkeley Unix 4.2BSD.>