Name
Title | ||
|---|---|---|
Efficient Depth Map Estimation Method Based On Gradient Weight Cost Aggregation Strategy For Distributed Video Sensor Networks |
Abstract | ||
|---|---|---|
Video sensor networking technologies have developed very rapidly in the last ten years. In this paper, a cross-based framework strategy for cost aggregation is presented for the depth map estimation based on video sensor networks. We formulate the process as a local regression problem consisting of two main steps with a pair of video sensors. The first step is to calculate estimates for a set of points within a shape-adaptive local support region. The second step is to aggregate the matching cost for the gradient-based weight of the support region at the outmost pixel. The proposed algorithm achieves strong results in an efficient manner using the two main steps. We have achieved improvement of up to 6.9%, 8.4%, and 8.3%, when compared with adaptive support weight (ASW) algorithm. Comparing to cross-based algorithm, the proposed algorithm gives 2.0%, 1.3%, and 1.0% in terms of nonocclusion, all, and discontinuities, respectively. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1155/2014/326029 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
Field | DocType | Volume |
Data mining,Classification of discontinuities,Video sensors,Computer science,Algorithm,Local regression,Sensor networking,Cost aggregation,Pixel,Depth map,Wireless sensor network,Distributed computing | Journal | 2014 |
ISSN | Citations | PageRank |
1550-1477 | 2 | 0.36 |
References | Authors | |
35 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gwang-Soo Hong | 1 | 21 | 5.53 |
| Byung-Gyu Kim | 2 | 396 | 39.17 |
| Kee-Koo Kwon | 3 | 25 | 6.26 |