Improved Weighted Median Filter with Superpixel for Disparity Refinement
Yuli Fei, Li Cao
Available Online May 2019.
- 10.2991/cnci-19.2019.16How to use a DOI?
- Stereo Matching, Disparity Refinement, Superpixel Segmentation
Stereo matching cannot get high accuracy for disparity estimations, especially at depth boundaries and textureless regions. To solve the problems, a disparity refinement method based on superpixel segmentation is proposed. A weighted median filter with superpixel information is designed. We give a penalty factor for the neighborhood pixels that are not within the same superpixel. Some experiments are done on the Middlebury dataset. The results show that the proposed method can reduce the mismatch rates around occlusion regions and textureless regions, and obtain a highly accurate disparity map.
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yuli Fei AU - Li Cao PY - 2019/05 DA - 2019/05 TI - Improved Weighted Median Filter with Superpixel for Disparity Refinement BT - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019) PB - Atlantis Press SP - 119 EP - 124 SN - 2352-538X UR - https://doi.org/10.2991/cnci-19.2019.16 DO - 10.2991/cnci-19.2019.16 ID - Fei2019/05 ER -