Dense Correspondence Using Non-Local DAISY Forest
- Publication Type:
- Conference Proceeding
- Citation:
- 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015, 2015
- Issue Date:
- 2015-01-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Xiaoshui_DICTA.pdf | Published version | 3.46 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2015 IEEE. Dense correspondence computation is a critical computer vision task with many applications. The most existing dense correspondence methods consider all the neighbors connected to the center pixels and use local support region. However, such approach might only achieve a locally-optimal solution.In this paper, we propose a non-local dense correspondence computation method by calculating the match cost on a tree structure. It is non-local because all other nodes on the tree contribute to the match cost computing for the current node. The proposed method consists of three steps, namely: 1) DAISY descriptor computation, 2) edge-preserving segmentation and forest construction, 3) PatchMatch fast search. We test our algorithm on the Middlebury and Moseg datasets. The results show that the proposed method outperforms the state-of-The-Art methods in dense correspondence computing and has a low computation complexity.
Please use this identifier to cite or link to this item: