Name
Title | ||
|---|---|---|
Reduced dimensionality space for post placement quality inspection of components based on neural networks |
Abstract | ||
|---|---|---|
The emergence of surface mount technology devices has resulted in several important advantages including increased component density and size reduction on the printed circuit board, on the expense of quality inspection. Classical visual inspection techniques require time-consuming image processing to improve the accuracy of the inspected results. In this paper we reduce the computational complexity of classical machine vision approaches by proposing two neural network based techniques. In the first we maintain image information only in the form of edges, whereas the second we preserve the entire content of info but compressed in a single dimension through image projections. Both algorithms are tested on real industrial data. The quality of inspection is preserved while reducing the computational time. |
Year | Venue | Keywords |
|---|---|---|
2004 | ESANN | computational complexity,image processing,machine vision,visual inspection,printed circuit board,neural network |
Field | DocType | Citations |
Visual inspection,Machine vision,Computer science,Printed circuit board,Image processing,Artificial intelligence,Artificial neural network,Automated optical inspection,Computer vision,Pattern recognition,Curse of dimensionality,Machine learning,Computational complexity theory | Conference | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefanos Goumas | 1 | 6 | 1.46 |
| Michalis E. Zervakis | 2 | 94 | 17.52 |
| George A. Rovithakis | 3 | 749 | 45.73 |