NDLI: A Novel Disparity Refinement Method Based on Semi-Global Matching Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Xie, Yuechao Zeng, Siyu Chen, Long
Copyright Year	2014
Abstract	Depth information can be obtained using stereo matching algorithms, which compute the horizontal displacement (disparity) of the corresponding points and convert to depth information using the triangular relation. However, the matching process is challenging with presence of textureless regions. This paper proposes a novel disparity refinement method for stereo matching based on Semi-global Matching (SGM) algorithm for texture less images. To be brief, the SGM algorithm is a high-performance matching algorithm of a stereo image pair and it reaches a trade off between matching quality and computing complexity. The main contribution comes from the improvement of matching quality on texture less regions. At the end of SGM, a right-to-left consistency check is performed to remove the invalid pixels in the disparity map. The proposed method is added after the right-to-left consistent check. We assume that the texture less regions in the original stereo pairs are planar. We employ edge detection to extract the textureless regions. The fitting method is employed in horizontal and vertical directions respectively. If the distance of two adjacent edge pixels is sufficiently large, the corresponding line segment will be considered as texturelss. For every texturelss line segment, the RANSAC algorithm is performed on the corresponding line segment in the disparity map. We use the well-known Middlebury dataset to compare our method with the normal SGM and other matching algorithms. It shows that our method performs well for most texture less stereo pairs.
Starting Page	1135
Ending Page	1142
File Size	1423846
Page Count	8
File Format	PDF
e-ISBN	9781479942749
DOI	10.1109/ICDMW.2014.126
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stereo Vision Matching Image segmentation Image edge detection Conferences SGM Cameras Radiometry Robustness Stereo vision
Content Type	Text
Resource Type	Article

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