Segment-Based Disparity Refinement With Occlusion Handling for Stereo Matching

In this paper, we propose a disparity refinement method that directly refines the winner-take-all (WTA) disparity map by exploring its statistical significance. According to the primary steps of the segment-based stereo matching, the reference image is over-segmented into superpixels and a disparity...

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Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 28(2019), 8 vom: 07. Aug., Seite 3885-3897
1. Verfasser: Yan, Tingman (VerfasserIn)
Weitere Verfasser: Gan, Yangzhou, Xia, Zeyang, Zhao, Qunfei
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2019
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
Beschreibung
Zusammenfassung:In this paper, we propose a disparity refinement method that directly refines the winner-take-all (WTA) disparity map by exploring its statistical significance. According to the primary steps of the segment-based stereo matching, the reference image is over-segmented into superpixels and a disparity plane is fitted for each superpixel by an improved random sample consensus (RANSAC). We design a two-layer optimization to refine the disparity plane. In the global optimization, mean disparities of superpixels are estimated by Markov random field (MRF) inference, and then, a 3D neighborhood system is derived from the mean disparities for occlusion handling. In the local optimization, a probability model exploiting Bayesian inference and Bayesian prediction is adopted and achieves second-order smoothness implicitly among 3D neighbors. The two-layer optimization is a pure disparity refinement method because no correlation information between stereo image pairs is demanded during the refinement. Experimental results on the Middlebury and KITTI datasets demonstrate that the proposed method can perform accurate stereo matching with a faster speed and handle the occlusion effectively. It can be indicated that the "matching cost computation + disparity refinement" framework is a possible solution to produce accurate disparity map at low computational cost
Beschreibung:Date Revised 24.06.2019
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1941-0042
DOI:10.1109/TIP.2019.2903318