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2017 | OriginalPaper | Chapter

Disparity Refinement Using Merged Super-Pixels for Stereo Matching

Authors : Jianyu Heng, Zhenyu Xu, Yunan Zheng, Yiguang Liu

Published in: Image and Graphics

Publisher: Springer International Publishing

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Abstract

The traditional disparity refinement methods cannot get highly accurate disparity estimations, especially pixels around depth boundaries and within low textured regions. To tackle this problem, two novel stereo refinement strategies are proposed: (1) merging super-pixels into stable region to maintain continuity and accuracy of the same disparity; (2) optimizing the co-operative relations between adjacent regions. Then we can obtain high-quality and high-density disparity maps. The quantitative evaluation on Middlebury benchmark shows that our algorithm can significantly refine the results obtained by local and non-local methods.

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Metadata
Title
Disparity Refinement Using Merged Super-Pixels for Stereo Matching
Authors
Jianyu Heng
Zhenyu Xu
Yunan Zheng
Yiguang Liu
Copyright Year
2017
Book
Image and Graphics

Print ISBN: 978-3-319-71606-0

Electronic ISBN: 978-3-319-71607-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71607-7_26

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