Window-based matching such as normalized cross-correlation (NCC) can reliably estimate depth even when the constant brightness assumption is violated in stereo due to imaging noise or different camera gains. However, fixed window methods tend to have poor performance at depth discontinuities and in low-texture regions. In this paper, we describes a novel learning-based algorithm, for stereo matching. The algorithm is based on the observation that the matching behavior of each expert is determined by the image texture and the underlying scene structure. In the proposed approach, the behaviors of multiple experts are first learned from ground truth using a simple histogrambased method and the likelihood under each expert is then combined probabilistically into a global MAP-MRF depth estimation framework. Since the resultant likelihood is a function of both stereo image and scene depth in a large neighboring area, we present an iterative Metropolis-Hastings algorithm for the MAP estimation that alternates between predicting expert behaviors and updating the disparity map. The experimental results show that our algorithm is comparable with state-of-the-art methods when the stereo images have identical intensity level but outperforms them when the intensities vary.
Later on, Kong and Tao REF extended their work by learning multiple experts from different matching windows sizes and centers, and the likelihood under each expert was then combined probabilistically into a global MRF framework for improving accuracy.
H.: Stereo matching via learning multiple experts behaviors
In: BMVC
Computer Science