Improving segment based stereo matching using SURF key points

State-of-the-art stereo matching algorithms estimate disparities using local block-matching, and subsequently refine the disparity estimates by introducing smoothness constraints and performing global energy minimization. Such algorithms are hampered by the inability of local block-matching algorithms to deal with repetitive patterns. This paper presents an approach that overcomes this problem by incorporating the disparity obtained from matching SURF key points between stereo image pairs. The algorithm provides further robustness to problems with repetitive pattern by penalizing the discrepancy between the initial and final disparity estimates in the global energy minimization. Evaluation of our approach on the Middleburry data set results shows that the our approach is more robust against repetitive patterns than existing approaches.