Presents an algorithmic approach to the problem of detecting independently moving objects in 3D scenes that are viewed under camera motion. There are two fundamental constraints that can be exploited for the problem: (i) a two- (or multi-)view camera motion constraint (for instance, the epipolar/trilinear constraint), and (ii) a shape constancy constraint. Previous approaches to the problem either only used partial constraints or relied on dense correspondences or flow. We employ both of these fundamental constraints in an algorithm that does not demand a-priori availability of correspondences or flow. Our approach uses the plane-plus-parallax decomposition to enforce the two constraints. It is also demonstrated, for a class of scenes called sparse 3D scenes, in which genuine parallax and independent motions may be confounded, how the plane-plus-parallax decomposition allows progressive introduction and verification of the fundamental constraints. The results of applying the algorithm to some difficult sparse 3D scenes look promising.

We present a validation study of an optical-flow method for the rapid estimation of myocardial displacement in magnetic resonance tagged cardiac images. This registration and change visualization (RCV) software uses a hierarchical estimation technique to compute the flow field that describes the warping of an image of one cardiac phase into alignment with the next. This method overcomes the requirement of constant pixel intensity in standard optical-flow methods by preprocessing the input images to reduce any intensity bias which results from the reduction in stripe contrast throughout the cardiac cycle. To validate the method, SPAMM-tagged images were acquired of a silicon gel phantom with simulated rotational motion. The pixel displacement was estimated with the RCV method and the error in pixel tracking was <4% 1000 ms after application of the tags, and after 30 degrees of rotation. An additional study was performed using a SPAMM-tagged multiphase slice of a canine left ventricle. The true displacement was determined using a previously validated active contour model (snakes). The error between methods was 6.7% at end systole. The RCV method has the advantage of tracking all pixels in the image in a substantially shorter period than the snakes method.

The ability to locate scenes and objects visible in aerial video imagery with their corresponding locations and coordinates in a reference coordinate system is important in visually-guided navigation, surveillance and monitoring systems. However, a key technical problem of locating objects and scenes in a video with their geo-coordinates needs to be solved in order to ascertain the geo-location of objects seen from the camera platform's current location. We present the key algorithms for the problem of accurate mapping between camera coordinates and geo-coordinates, called geo-spatial registration. Current systems for geo-location use the position and attitude information for the moving platform in some fixed world coordinates to locate the video frames in the reference database. However, the accuracy achieved is only of the order of 100s of pixels. Our approach utilizes the imagery and terrain information contained in the geo-spatial database to precisely align dynamic videos with the reference imagery and thus achieves a much higher accuracy. Applications of geo-spatial registration include aerial mapping, target location and tracking and enhanced visualization such as the overlay of textual/graphical annotations of objects of interest in the current video using the stored annotations in the reference database.

The technical foundation for many applications of computer vision to multimedia applications is efficient and robust image motion estimation. These algorithms enable the creation of algorithms for mosaic construction, registration of video to a database multi-sensor registration, 3D estimation and representation, and video content indexing and retrieval. Demonstrations on the above topics will be shown at WACV'98 by the Media Vision Group of Sarnoff Corporation.

This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endeavour stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement. In particular, it is known in the biomedical community that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body its appearance is amenable to remote examination with the aid of a computer vision system. The body of this paper details the design and operation of such a system. Also presented are the results of an empirical study where the system exhibits flawless performance in the evaluation of 520 iris images.>