Stereo　matching　aims　to　obtain　depth　information　of　scene　from　captured　images,　which　becomes　an　active　research　topic　in　the　field　of　computer　vision.　Most　stereo　matching　cost　algorithms　are　based　on　a　common　assumption,　which　is　the　intensity　or　color　value　of　corresponding　pixels　are　same.　However,　in　real-world　applications,　the　colors　of　the　objects　observed　in　the　recorded　image　data　are　affected　by　radiometric　variations.　In　this　paper,　using　a　novel　similarity　measure　we　propose　a　robust　stereo　matching　method,　which　has　robust　performance　to　noise,　illumination　condition　changes,　and　exposure　changes　between　left　and　right　images.　The　proposed　stereo　matching　cost　combines　improved　zero　mean　normalized　cross-correlation　(ZNCC)　model　and　the　absolute　difference　of　local　binary　pattern　(LBP)　of　windows　to　get　both　the　color　and　texture　similarity　of　windows　to　be　matched.　Based　on　Middleburry　data　set,　we　verify　the　effectiveness　of　the　proposed　algorithm.　Computed　results　show　that　the　proposed　algorithm　is　more　robust　to　illumination　changes　and　noise　than　related　stereo　matching　algorithms.　©　2020　Association　for　Computing　Machinery.