title: Bayesian Stereo Matching Method Based on Edge Constraints.
abstract: A new global stereo matching method is presented that focuses on the handling of disparity, discontinuity and occlusion. The Bayesian approach is utilized for dense stereo matching problem formulated as a maximum a posteriori Markov Random Field (MAP-MRF) problem. In order to improve stereo matching performance, edges are incorporated into the Bayesian model as a soft constraint. Accelerated belief propagation is applied to obtain the maximum a posteriori estimates in the Markov random field. The proposed algorithm is evaluated using the Middlebury stereo benchmark. Our experimental results comparing with some state-of-the-art stereo matching methods demonstrate that the proposed method provides superior disparity maps with a subpixel precision.
authors.affiliation: School of Electronic Information, Wuhan University, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Li
authors.affiliation: School of Electronic Information, Wuhan University, China.
authors.forename: Wenxuan
authors.initials: W
authors.lastname: Shi
authors.affiliation: School of Electronic Information, Wuhan University, China.
authors.forename: Dexiang
authors.initials: D
authors.lastname: Deng
authors.affiliation: Department of Neurosurgery, University of Pittsburgh, USA.
authors.forename: Wenyan
authors.initials: W
authors.lastname: Jia
authors.affiliation: Departments of Neurosurgery, Electrical engineering and Bioengineering, University of Pittsburgh, USA.
authors.forename: Mingui
authors.initials: M
authors.lastname: Sun
keywords: MAP-MRF; belief propagation; edge classify; local affine model