Name
Abstract | ||
|---|---|---|
A local character tensor is proposed for the automatic three-dimensional (3D) pair-wise registration based on free-view 3D datasets. In the proposed method, there are two characters, i.e., the optimal segmentation to realize the automatic processing and local character tensor to improve the matching probability. It is applied for solving the mismatching problem and large-scale 3D datasets, using non-structured datasets are tested in a PC with Intel Pentium M 1.50 GHz and 1.0 GB memory. Pair-wised experimental results show the proposed method increases average 12.6% matching probability and decreases average 18.9 seconds computational time compared to the conventional local character based registration method. This registration method can be further applied to 3D reconstruction from navigation, model based object recognition to accurate 3D geometric object model application. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.20965/jaciii.2007.p0848 | JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS |
Keywords | Field | DocType |
3D registration, Sutherland Hodgman segmentation, pair-registration, tensor, matching | Computer vision,Tensor,Pattern recognition,Computer science,Artificial intelligence | Journal |
Volume | Issue | ISSN |
11 | 7 | 1343-0130 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingjing Wang | 1 | 0 | 0.34 |
| Fangyan Dong | 2 | 453 | 54.77 |
| Yutaka Hatakeyama | 3 | 67 | 12.29 |
| Hajime Nobuhara | 4 | 192 | 34.02 |
| Kaoru Hirota | 5 | 1634 | 195.49 |