Stereo Matching Using Sub-segmentation and Robust Higher-Order Graph Cut

Xie, Yiran; Liu, Nianjun; Liu, Sheng; Barnes, Nick

doi:10.1109/dicta.2011.93

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Therefore, Kohli's approach is unable to be applied in depth estimation directly. Xie et al [16] improved the higher order term proposed by Kohli et al and applied it to the depth estimation successfully. The improved higher order term enforces impliedly the assumption that all the segments of the input image are regarded as various planes.…”

Section: Introductionmentioning

confidence: 99%

Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes

Liu

Jin

Mao

et al. 2013

The Scientific World Journal

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This paper proposes a segmentation-based global optimization method for depth estimation. Firstly, for obtaining accurate matching cost, the original local stereo matching approach based on self-adapting matching window is integrated with two matching cost optimization strategies aiming at handling both borders and occlusion regions. Secondly, we employ a comprehensive smooth term to satisfy diverse smoothness request in real scene. Thirdly, a selective segmentation term is used for enforcing the plane trend constraints selectively on the corresponding segments to further improve the accuracy of depth results from object level. Experiments on the Middlebury image pairs show that the proposed global optimization approach is considerably competitive with other state-of-the-art matching approaches.

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Section: Introductionmentioning

confidence: 99%