This paper presents a method to detect and construct a 3D geometric model of an urban area with complex buildings using aerial LIDAR (Light Detection and Ranging) data. The LIDAR data collected from a nadir direction is a point cloud containing surface samples of not only the building roofs and terrain but also undesirable clutter from trees, cars, etc. The main contribution of this work is the automatic recognition and estimation of simple parametric shapes that can be combined to model very complex buildings from aerial LIDAR data. The main components of the detection and modeling algorithms are (i) Segmentation of roof and terrain points. (ii) Roof topology Inference. We introduce the concept of a roof-topology graph to represent the relationships between the various planar patches of a complex roof structure. (iii) Parametric roof composition. Simple parametric roof shapes that can be combined to create a complex roof structure of a building are recognized by searching for sub-graphs in its roof-topology graph. (iv) Terrain Modeling. The terrain is identified and modeled as a triangulated mesh. Finally, we provide experimental results that demonstrate the validity of our approach for rapid and automatic building detection and geometric modeling with real LIDAR data. We are able to model cities and other urban areas at the rate of about 10 minutes per sq. mile on a low-end PC.

In this paper we present methods for exploitation and enhanced visualization of video given a prior coarse untextured polyhedral model of a scene. Since it is necessary to estimate the 3D poses of the moving camera, we develop an algorithm where tracked features are used to predict the pose between frames and the predicted poses are refined by a coarse to fine process of aligning projected 3D model line segments to oriented image gradient energy pyramids. The estimated poses can be used to update the model with information derived from video, and to re-project and visualize the video from different points of view with a larger scene context. Via image registration, we update the placement of objects in the model and the 3D shape of new or erroneously modeled objects, then map video texture to the model. Experimental results are presented for long aerial and ground level videos of a large-scale urban scene.

There is great interest in the storage and browsing of large collections of video clips, due to the current explosion in demand for digital video. Many new algorithms and standards (MPEG, H.261, H.263, etc.) have been developed in recent years for the coding of video. However, most of these techniques lose performance when there exists significant camera motion, a common element of some classes of video such as surveillance and sporting events. A mosaic-based approach to video compression, initially presented by Irani, Hsu, and Anandan (1995), creates a world-oriented panoramic representation of the scene for compression. This paper proposes and evaluates a specific mosaic-based algorithm for video compression that uses a static mosaic with a wavelet-based coding scheme for the mosaic and the temporal residual information. The mosaic-based approach is compared to a global motion compensation approach and the algorithms detailed in this paper are compared to the H.263 TMN5 coder.

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.>