A stereo vision algorithm suitable for fast dense stereo matching is proposed. This method extracts areas of uniform disparity or dense disparity features by testing candidate disparity estimates and classifying pixels according to a matching error. By commencing with a rigorous error threshold, pixels of reliable disparity values are identified. As the threshold is relaxed, other pixels are aggregated, forming dense areas of uniform disparity. Neighboring support is acquired with texture filling and other morphological operations in each disparity feature. The algorithm is tested on publicly available and self-generated data sets and is shown to produce promising results in terms of quality and speed. Handling of regions without texture is discussed and a comparison with other methods is also presented.