We present a newly developed airborne InSAR system incorporating a novel phase unwrapping algorithm, capable of retrieving a highly accurate Digital Surface Model (DSM). The SAR sensor system, with a spatial resolution of 30 cm, is carried on an airborne platform which has 2 antennas placed in a baseline length of 1 m. We have established a DSM reconstruction processing technique, which includes the new "Iterated Conditional Modes-Minimum Cost Flow" (ICM-MCF) phase-unwrapping algorithm. The ICM-MCF algorithm finds a locally optimal configuration of unwrapped phases under a well-characterized statistical model of the terrain and noise. An experimental field observation was carried out in Tsukuba, Japan. The DSM was generated, and the height accuracy of the SAR-DSM was evaluated by comparing with laser profiler data. For 50 cm x 50 cm mesh, an accuracy of better than 50 cm in height was confirmed.

Pfinder is a real-time system for tracking people and interpreting their behavior. It runs at 10 Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions. Pfinder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding.

Ten different feature vectors are tested in a gesture recognition task which utilizes 3D data gathered in real-time from stereo video cameras, and HMMs for learning and recognition of gestures. Results indicate velocity features are superior to positional features, and partial rotational invariance is sufficient for good performance.

Pfinder is a real-time system for tracking and interpretation of people. It runs on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multi-class statistical model of color and shape to obtain a 2-D representation of head and hands in a wide range of viewing conditions. These representations are useful for applications such as wireless interfaces, video databases, and low-bandwidth coding, without cumbersome wires or attached sensors.

We describe a method for estimation of 3D geometry from 2D blob features. Blob features are clusters of similar pixels in the image plane and can arise from similarity of color, texture, motion and other signal-based metrics. The motivation for considering such features comes from recent successes in real-time extraction and tracking of such blob features in complex cluttered scenes in which traditional feature finders fail, e.g. scenes containing moving people. We use nonlinear modeling and a combination of iterative and recursive estimation methods to recover 3D geometry from blob correspondences across multiple images. The 3D geometry includes the 3D shapes, translations, and orientations of blobs and the relative orientation of the cameras. Using this technique, we have developed a real-time wide-baseline stereo person tracking system which can self-calibrate itself from watching a moving person and can subsequently track people's head and hands with RIMS errors of 1-2 cm in translation and 2 degrees in rotation. The blob formulation is efficient and reliable, running at 20-30 Hz on a pair of SGI Indy R4400 workstations with no special hardware.

Presents a formulation for recursive recovery of motion, pointwise structure, and focal length from feature correspondences tracked through an image sequence. In addition to adding focal length to the state vector, several representational improvements are made over earlier structure from motion formulations, yielding a stable and accurate estimation framework which applies uniformly to both true perspective and orthographic projection. Results on synthetic and real imagery illustrate the performance of the estimator.>

We are interested in being able to take classic film characters, or video of current-day personalities, and produce computer models and animations of them by automatic analysis of the video or film footage. In this paper we survey our progress toward producing such automatic modeling and animation systems.>

A recursive estimation technique for recovering the 3-D motion and pointwise structure of an object is presented. It is based on the use of relative orientation constraints in a local coordinate frame. By carefully formulating the problem to propagate all constraints and to use the minimal number of parameters, an estimator is obtained which is remarkably accurate, stable, and fast-conveying. Numerous experiments using both real and synthetic data demonstrate structure recovery with a typical error of 1.5% and typical motion recovery errors of 1% in translation and 2/spl deg/ in rotation.>

Interactive graphics systems that are driven by visual input are discussed. The underlying computer vision techniques and a theoretical formulation that addresses issues of accuracy, computational efficiency, and compensation for display latency are presented. Experimental results quantitatively compare the accuracy of the visual technique with traditional sensing. An extension to the basic technique to include structure recovery is discussed.>