To　solve　the　matching　problem　in　textureless　and　occluded　regions,　a　stereo　matching　algorithm　was　presented　in　this　paper,　which　integrated　segmentation　clues　and　convolutional　neural　network　(CNN)　matching　results　into　an　Markov　random　field　(MRF)　energy　function.　The　unary　term　of　the　energy　function　was　formulated　by　the　output　of　CNN,　which　was　powerful　in　feature　expression.　The　pairwise　smooth　term　was　defined　by　the　over-segmentation　results　of　the　input　images　to　constrain　matching　in　textureless　and　occluded　regions.　Experiments　on　Middlebury　and　KITTI　datasets　validated　the　performances　of　the　algorithm　and　its　components.　Results　show　that　the　algorithm　outperforms　the　other　methods　proposed　in　recent　years,　especially　in　dealing　with　textureless　and　occluded　regions.　©　2019,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.