Stereo matching is a long active topic and difficult problem in computer vision, and is a crucial technique in stereovision. An algorithm by combining initial matching via segmentation-based variable support with greedy disparity estimation as post-processing is proposed to resolve the ambiguity of binocular stereo problem in a local perspective. Firstly, color segmentation is conducted on both stereo images, and segmentation-based adaptive support weight is assigned for each pixel to eliminate ambiguity in feature matching, and then matching cost with the variable support is calculated to obtain initial disparity. Secondly, to address more other complex ambiguity in low textured and repetitive patterns or large occluded regions etc., greedy disparity estimation procedure consists sequentially of three steps: segmentation-based disparity calibration, narrow occlusion handling and multi-directional weighted least square fitting. The experimental results indicate that this technique with segmentation cues can increase robustness against outliers and obtain accurate and dense disparity effectively. It’s concise and efficient.