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This paper investigates the use of a region-based approach for the stereo matching problem. We have stated this problem in a commonly adopted global energy-based framework. Our energy-based model mixes a local and robust regularization term with global spatial constraints. These constraints are related to a (precomputed) partition into homogeneous regions with identical disparity. In practice, our approach assigns a single disparity to regions instead of individual pixels. These regions, used to globally constrain the ill-posed nature of our minimization problem, are estimated by combining an unsupervised Markovian segmentation and a roughly estimated disparity map. This disparity map is computed with a basic winner-take-all (WTA) procedure. The proposed global energy function seems to be well suited to find good disparity discontinuities at object boundaries, especially when the number of disparities is large. An iterated conditional modes (ICM) algorithm is used to optimize this global energy function. We provide experimental results on real stereo image pairs. A quality measure, based on ground truth data, is used to evaluate the performance of our algorithm. Results indicate that our approach is fast and performs well compared to other existing methods.