Window-based approach for fast stereo correspondence

In this study, the authors present a new area-based stereo matching algorithm that computes dense disparity maps for a real-time vision system. Although many stereo matching algorithms have been proposed in recent years, correlation-based algorithms still have an edge because of speed and less memory requirements. The selection of appropriate shape and size of the matching window is a difficult problem for correlation-based algorithms. In the proposed approach, two correlation windows are used to improve the performance of the algorithm while maintaining its real-time suitability. The CPU implementation of the proposed algorithm computes more than 10 frame/s. Unlike other area-based stereo matching algorithms, this method works very well at disparity boundaries as well as in low textured image areas and computes a dense and sharp disparity map. Evaluations on the benchmark Middlebury stereo datasets have been performed to demonstrate the qualitative and quantitative performance of the proposed algorithm.