In several recent movie blockbusters, including Enemy of the State and Patriot Games, scientists magnify digital satellite imagery to determine the identities of people captured on surveillance video. These individuals appear rather blocky at the coarsest resolution scales due to undersampling by the image sensor array, but they become remarkably clear after zooming in on a particular region-of-interest. Obviously, real-world video enhancement algorithms are simply not capable of calculating the perfect results produced in Hollywood. However, additional detail can be extracted from an image sequence by integrating several adjacent digital video frames to compute a high-resolution video still. Extensive prior research has been conducted on the Bayesian super-resolution enhancement of visible light image sequences. For night vision applications, super-resolution enhancement must be applied to image sequences with frames acquired in the infrared region of the electromagnetic spectrum. Most of these applications are DoD-related (military), and they include the enhancement of infrared reconnaissance and surveillance imagery, as well as the enhancement of infrared imagery for automated target tracking and real time multisensor fusion. Several computational simulations have been performed, which visually depict how well the proposed technique enhances short night vision image sequences consisting of two-to-five frames This technique is capable of increasing resolution by factors up to 16/spl times/, although video stills appear somewhat blocky at higher magnification factors.

Personal digital assistants (PDAs) are becoming commonplace, and feature continually increasing processing and storage capabilities. Distribution of media through wireless means to these devices is becoming less expensive, but is not yet widespread. The authors describe a novel communication infrastructure to provide distribution of information to PDAs utilizing inexpensive methods; our approach is both scalable and flexible. Most PDAs support infrared (IR) communication, and we focus on this; our infrastructure is not restricted to this, however, and is adaptable to the emerging wireless communication technologies.

Presents a new method for determining optimal paths in a weighted and dynamic 2D environment, together with some simulation results. After mapping the dynamic and weighted 2D environment onto a static 3D space-time workspace, we represent the 3D workspace by a weighted and framed octree (wf-octree), and find optimal paths in the weighted and dynamic 2D environment by propagating a diamond-shaped path planning wave in the 3D workspace through the uniformly weighted leaf nodes of the wf-octree. A wave propagation heap is introduced to control the process of the wave propagation. Based on interesting data-structural and computational geometric techniques, our approach propagates the path-planning wave through each uniformly-weighted and framed octant efficiently. Our approach guarantees the optimality of the resulting paths without requiring the entire static 3D space-time workspace to be searched. Therefore, it is much more efficient than other commonly-used cell decomposition methods such as the grid-based ones, while it achieves at least the same accuracy as other cell decomposition methods. The distance we compute is based on the L/sub 1/ or L/sub /spl infin// metric.

In this paper we investigate the problem of finding a Euclidean (L/sub 2/) shortest path between two distinct locations in a planar environment. We propose a novel cell decomposition approach which calculates an L/sub 2/ distance transform through the use of a circular path-planning wave. The proposed method is based on a new data structure, called the framed-quadtree, which combines together the accuracy of high resolution grid-based path planning techniques with the efficiency of quadtree-based techniques, hence having the advantages of both. The heart of this method is a linear time algorithm for computing certain special dynamic Voronoi diagrams. The proposed method does not place any unrealistic constraints on obstacles or on the environment and represents an improvement in accuracy and efficiency over traditional path planning approaches in this area.

A conditional shortest path is a collision-free path of shortest distance based on known information on an obstacle-scattered environment at a given time. This paper investigates the problem of finding a conditional L/sub 2/ shortest path through an unknown environment in which path planning is implemented "on the fly" as new obstacle information becomes available through external sensors. We propose a novel cell decomposition approach which calculates an L/sub 2/ distance transform through the use of a circular path-planning wave. The proposed method is based on a new data structure, called the framed-quadtree, which combines together the accuracy of grid-based path planning techniques with the efficiency of quadtree-based techniques, hence having the advantages of both. The heart of this method is a linear time algorithm for computing dynamic Voronoi diagrams.