The 3D Mosaic system is a vision system complex 3D scenes from a sequence of images obtained from The system encompasses several levels of the vision process, starting with images and ending symbolic scene This paper describes the various components of the system, stereo analysis, monocular analysis, and constructing and the scene model. In addition, the representation of the scene model described. This model is intended for such matching, generation, planning paths through the scene, and making other deciswns about the scene Examples showing how system is used to interpret aerial photographs of urban are presented. Each view of the scene, may be either a single image or a stereo pair, undergoes analysis which in a wire-frame description represents portions of edges ond vertices of objects. The model

This paper presents a stereo algorithm using dynamic programming technique. The stereo matching problem, that is, obtaining a correspondence between right and left images, can be cast as a search problem. When a pair of stereo images is rectified, pairs of corresponding points can be searched for within the same scanlines. We call this search intra-scanline search. This intra-scanline search can be treated as the problem of finding a matching path on a two dimensional (2D) search plane whose axes are the right and left scanlines. Vertically connected edges in the images provide consistency constraints across the 2D search planes. Inter-scanline search in a three-dimensional (3D) search space, which is a stack of the 2D search planes, is needed to utilize this constraint. Our stereo matching algorithm uses edge-delimited intervals as elements to be matched, and employs the above mentioned two searches: one is inter-scanline search for possible correspondences of connected edges in right and left images and the other is intra-scanline search for correspondences of edge-delimited intervals on each scanline pair. Dynamic programming is used for both searches which proceed simultaneously in two levels: the former supplies the consistency constraints to the latter while the latter supplies the matching score to the former. An interval-based similarity metric is used to compute the score. 1.