Stereo matching is a fundamental topic in computer vision. Usually, stereo matching is mainly composed of four stages: cost computation, cost aggregation, disparity optimization and disparity refinement. In this paper, we propose a novel stereo matching method with space-constrained cost aggregation and segmentation-based disparity refinement. Stateof- the-art methods are used for cost aggregation and disparity optimization stages. Three technical contributions are given in this paper. First, applying space-constrained cross-region in cost aggregation stage; second, utilizing both color and disparity information in image segmentation; third, using image segmentation and occlusion region detection to aid disparity refinement. The performance of our platform ranks second in the Middlebury evaluation.
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Yi Peng ; Ge Li ; Ronggang Wang and Wenmin Wang
Stereo matching with space-constrained cost aggregation and segmentation-based disparity refinement
", Proc. SPIE 9393, Three-Dimensional Image Processing, Measurement (3DIPM), and Applications 2015, 939309 (March 17, 2015); doi:10.1117/12.2083741; http://dx.doi.org/10.1117/12.2083741