In this paper, we present a multi-scale Gabor phase-based stereo matching scheme. Unlike the mechanism in the existing phase-based stereo matching methods, where disparity is formulated as the ratio of phase difference between two views to the local frequency at the given position, we set up a robust data measure from multi-scale Gabor phases to greatly alleviate the negative effect of phase singularity. A cost function is then advanced based on this robust data measure. To further improve the accuracy of disparity estimation, we formulate the cost function as three coupled Markov Random Field (MRF) cost terms in frequency domain. To obtain globally optimized disparity map in wide range, graph cut is employed to perform the minimization of the cost function. Compared with the state-of-the-art stereo matching methods, experimental results demonstrate that our approach gets comparable matching performance in indoor scenes and achieves much better results in aerial scenes.
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