Name
Abstract | ||
|---|---|---|
Accurate depth estimation is still an important challenge after a decade, particularly from stereo images. The accuracy comes from a good depth level and preserved structure. For this purpose, a depth post-processing framework is proposed in this paper. The framework starts with the "Adaptive Random Walk with Restart (2015)" algorithm. To refine the depth map generated by this method, we introduced a form of median solver/filter based on the concept of the mutual structure, which refers to the structural information in both images. This filter is further enhanced by a joint filter. Next, a transformation in image domain is introduced to remove the artifacts that cause distortion in the image. The proposed post-processing method is then compared with the top eight algorithms in the Middlebury benchmark. To explore how well this method is able to compete with more widely known techniques, a comparison is performed with Google's new depth map estimation method. The experimental results demonstrate the accuracy and efficiency of the proposed post-processing method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ACCESS.2016.2603220 | IEEE ACCESS |
Keywords | Field | DocType |
Stereo matching,depth map,accuracy,edge preserving | Computer vision,Median filter,Algorithm design,Computer science,Random walk,Artificial intelligence,Solver,Depth map,Image resolution,Distortion | Journal |
Volume | ISSN | Citations |
4 | 2169-3536 | 1 |
PageRank | References | Authors |
0.36 | 17 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hossein Javidnia | 1 | 10 | 4.71 |
| P. M. Corcoran | 2 | 414 | 82.56 |