Name
Title | ||
|---|---|---|
3D object recognition based on a geometrical topology model and extreme learning machine. |
Abstract | ||
|---|---|---|
In this paper, one geometrical topology hypothesis is present based on the optimal cognition principle, and the single-hidden layer feedforward neural network with extreme learning machine (ELM) is used for 3D object recognition. It is shown that the proposed approach can identify the inherent distribution and the dependence structure for each 3D object along multiple view angles by evaluating the local topological segments with a dipole topology model and developing the relevant mathematical criterion with ELM algorithm. The ELM ensemble is then used to combine the individual single-hidden layer feedforward neural network of each 3D object for performance improvements. The simulation results have shown the excellent performance and the effectiveness of the developed scheme. © 2012 Springer-Verlag London Limited. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s00521-012-0892-7 | Neural Computing and Applications |
Keywords | DocType | Volume |
Dipole topology,Extreme learning machines,Geometrical topology hypothesis,Optimal cognition principle | Journal | 22 |
Issue | ISSN | Citations |
3-4 | 1433-3058 | 4 |
PageRank | References | Authors |
0.43 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Nian | 1 | 159 | 12.18 |
| Bo He | 2 | 77 | 13.20 |
| Amaury Lendasse | 3 | 1876 | 126.03 |