Correlation algorithm with adaptive window for aerial image in stereo vision

Binocular stereo vision processes estimate 3D surfaces using a pair of images taken from different points of view. 3D surface characteristics are estimated by matching 2D image areas or features corresponding to the projections of same 3D points. The most classic area-based methods used cross correlation with a fixed window-size, but this technique presents a major drawback: the computation of depth is generally prone to errors close to surface discontinuities. In this paper, we present our current work on aerial stereo images of urban areas. A correlationbased algorithm, with an adaptive window-size constrained by an edge map extracted from the images, is presented. This technique is not used to refine an initial disparity map, but to create one with good discontinuities location. The algorithm follows 3 steps: first window sizes for each pixel are computed; then a disparity map is created; third, map completion is performed and a final dense disparity map with subpixel precision is produced by Kanade correction. Experimental results on real aerial images are presented.