Name
Abstract | ||
|---|---|---|
This paper presents a neural recognition system for manufacturing applications in difficult industrial environments. In such difficult environments, where objects to be recognized can be dirty and illumination conditions cannot be sufficiently controlled, the required accuracy and rigidity of the system are critical features. The purpose of the real-time system is to recognize air-conditioning objects for avoiding deficiency in the manufactured process and erroneous identifications due to a large variety of size and kinds of objects. The architecture of the proposed system is based on several backpropagation neural networks in order to make an automatic recognition. Experimental results of a large variety of air-conditioning objects are provided to show the performance of the neural system in a difficult environment. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02481-8_189 | IWANN (2) |
Keywords | Field | DocType |
manufactured objects,backpropagation neural network,difficult environment,automatic recognition,neural recognition system,proposed system,air-conditioning object,large variety,difficult industrial environment,real-time system,neural system,air conditioning,real time systems | Rigidity (psychology),Architecture,Recognition system,Computer science,Object type,Time delay neural network,Artificial intelligence,Artificial neural network,Backpropagation,Machine learning,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | Citations |
5518 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael M. Luque | 1 | 47 | 7.38 |
| Enrique Dominguez | 2 | 2 | 1.70 |
| Esteban J. Palomo | 3 | 95 | 14.79 |
| Jose Muñoz | 4 | 1 | 1.37 |