As a digital image provides such information about a scene, a disparity map can be yielded by means of stereo images. This topic was exhaustively surveyed but it remains one of the most important branches in both computer vision and machine vision. Most algorithms are organized in a pipeline that starts with a matching cost step and ends with a disparity refinement. This paper provides a simple but an effective method to adjust a disparity map in a more appropriate configuration, i.e. it presents a disparity refinement technique. It is based on an assumption that most disparities in a region point to a correct disparity value for this area. To develop the methodology, we use image segmentation and support weighted windows. By performing an evaluation, it shows that this method can increase the robustness of a raw disparity map even with a lot of noisy parts.