Disparity map determination by multiple adaptive windows

The correspondence problem is one of main topics in stereo vision that, despites being studied for many years, is still in progress. In this paper, we present a new method that computes the disparity map. Our method is local (i.e. only information gathered in the close neighborhood is used) and is based on image statistics. More specifically it combines multiple adaptive windows and local statistical measures in order to optimize the quality of the computation of disparity maps. As the size of windows is critical for local methods, we propose an algorithm that modifies the size of windows surrounding the pixel of interest to capture enough information in regions with low texture energy. This process is based on statistical measures (mean and horizontal/vertical average deviation) taken in the windows of the original stereo images. In the last section we show that our method performs very well compared to other existing local methods.