Vectorization, matching and simplification of image contours in uncalibrated aerial stereo-vision

摘要

We present in this paper, a method for vectorization, matching and simplification of image contours in aerial stereovision. The goal is to compute a 3D reconstruction of the scene. The advantage of our method is that it only requires the extracted bitmap contours in one image of the couple. This is quite interesting since bitmap contours extraction often requires large computation times. Moreover, contours of same objects seen from different locations may be quite different, making very difficult a direct matching. Hence, our matching is done over image points whereas over contours. The matching of contour points is done with a correlation technique using the couple of images. Once this is done, the linearized contours are simplified by only keeping corresponding points which are geometrically significant. Finally, a set of stereo-vectors is obtained which can be used in a stereo-viewer or to compute three-dimensional reconstruction. The efficiency of this process is tested on a difficult example of a stereo couple of urban area with a wide angle between the two views. We show that the results are very satisfying in terms of relevance of the reconstructed vectors, speed of the process and direct extensibility to parallel computing for very large images.