Fast stereo matching using two stage color-based segmentation and dynamic programming

A new method for fast stereo matching is presented in this paper. Our stereo algorithm relies on over-segmenting the source image. Computing match values over entire segments rather than single pixels provides robustness to noise and intensity bias. Color-based segmentation helps to split each image into regions that are likely to contain similar disparities. By employing a dynamic programming technique that applies regularization weights both along and across the scanlines, we solve the typical inter-scanline inconsistency problem. To adaptively determine regularization weight functions, we propose second-stage segmentation that assigns small weights to regions of two different segments to let their common boundary to be accounted as disparity jump. Combining over-segmentation and dynamic programming significantly speeds up stereo matching process while keeping matching results comparable to state-of-the-arts.