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An Augmented Virtual Environment (AVE) fuses dynamic imagery with 3D models. The AVE provides a unique approach to visualize and comprehend multiple streams of temporal data or images. Models are used as a 3D substrate for the visualization of temporal imagery, providing improved comprehension of scene activities. The core elements of AVE systems include(More)
Figure 1: Tracking and annotating an object using graph cut segmentation. A building sign on the USC campus is first detected using simple recognition, after which no additional information is needed. As the camera moves, we segment and track the sign through significant scale and orientation changes, rendering an annotation above it. This example(More)
In this paper we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory(gabor filtering, tensor voting) and optimized(More)
We present a real-time hybrid tracking system that integrates gyroscopes and line-based vision tracking technology. Gyroscope measurements are used to predict orientation and image line positions. Gyroscope drift is corrected by vision tracking. System robustness is achieved by using a heuristic control system to evaluate measurement quality and select(More)
The biggest single obstacle to building effective augmented reality (AR) systems is the lack of accurate wide-area sensors for trackers that report the locations and orientations of objects in an environment. Active (sensor-emitter) tracking technologies require powered-device installation, limiting their use to prepared areas that are relatively free of(More)