Name
Abstract | ||
|---|---|---|
Affected by scanning object, environment, scanning speed and user¿s operation .etc, some information of the object¿s surface can¿t be detected by the laser scanner. Aiming at the data loss in laser detecting , the paper presents an improved BP neural network based on GA for 3D laser data repairing, the novelty of this method is adopting Genetic Algorithm(GA) to optimize the configure and weight of network, and at the same time combining Back Propagation(BP) Algorithm to find optimal approximation. The simulation shows the improved BP neural network based on GA has a faster constringency speed and better repairing precision than traditional BP neural network and GA algorithm. Lastly, the paper gives the result of repairing the point cloud collected by 3D information reconstruction system using this network |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/RAMECH.2008.4690878 | RAM |
Keywords | Field | DocType |
bp network,ga,laser scanner,data repairing,genetic algorithm,neural network,artificial intelligence,point cloud,back propagation,neural networks,encoding,machine vision,artificial neural networks,gallium,genetics,approximation algorithms,lasers | Data loss,Control engineering,Artificial intelligence,Artificial neural network,Genetic algorithm,Computer vision,Approximation algorithm,Object detection,Laser scanning,Algorithm,Engineering,Point cloud,Backpropagation | Conference |
ISBN | Citations | PageRank |
978-1-4244-1676-9 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shouqian Yu | 1 | 8 | 2.45 |
| Lixia Rong | 2 | 0 | 0.68 |
| Chen Weihai | 3 | 190 | 38.21 |
| Xingming Wu | 4 | 43 | 13.16 |