Name
Abstract | ||
|---|---|---|
We propose a new edge preserve and depth image recovery method in RGB-D camera systems that gives a sharp and accurate object shape from a noisy boundary depth map. The edges of an input depth image are detected and the noisy pixels around them are removed from the depth image. An anisotropic diffusion edge tensor of an input RGB image is computed. Missing depth pixels are then recovered using the total generalized variation optimization with guidance of the RGB-image edge tensor. Thus, accurate object depth boundary can be obtained and well aligned with the object edges in RGB images. The missing or invalid depth pixels in the large hole areas and the thin object can also be recovered. Experimental results show the improvement in edge preserve and depth image recovery with the expense on computation complexity when compared with previous works. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICCE-Berlin.2014.7034303 | ICCE-Berlin |
Keywords | Field | DocType |
computational complexity,edge detection,image colour analysis,optimisation,tensors,rgb-d camera systems,rgb-image edge tensor,anisotropic diffusion edge tensor,computation complexity,depth image recovery method,edge preservation method,noisy boundary depth map,object depth boundary,total generalized variation optimization,rgb-d camera,anisotropic diffusion tensor,depth recovery,total generalized variation,optimization,tensile stress,anisotropic magnetoresistance,noise measurement | Anisotropic diffusion,Computer vision,Noise measurement,Tensor,Computer science,Stress (mechanics),Magnetoresistance,RGB color model,Pixel,Artificial intelligence,Depth map | Conference |
ISSN | Citations | PageRank |
2166-6814 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pongsak Lasang | 1 | 16 | 5.29 |
| Sheng Mei Shen | 2 | 131 | 13.13 |
| Wuttipong Kumwilaisak | 3 | 70 | 12.10 |