Stereo matching is a useful algorithm to infer depth information from two or more of images and has uses in mobile robotics, three-dimensional building mapping and three-dimensional reconstruction of objects. In area-based algorithms, the similarity between one pixel of an image (key frame) and one pixel of another image is measured using a correlation index computed on neighbors of these pixels (correlation windows). In order to preserve edges, the use of small correlation windows is necessary while for homogeneous areas, large windows are required. In addition, to improve the execution time, stereo matching algorithms often are implemented in dedicated hardware such as FPGA or GPU devices. In this article, we present an FPGA stereo matching processor based on the Sum of Hamming Distances (SHD). We propose a grayscale-based similarity criterion, which allows separating the objects and background from the correlation window. By using the similarity criterion, it is possible to improve the performance of any grayscale-based correlation coefficient and reach high performance for homogeneous areas and edges. The developed FPGA architecture reaches high performance compared to other real-time stereo matching algorithms, up to 10 % more accuracy and enables to increase the processing speed near to 20 megapixels per second.