Optical measurements provide a non-invasive method for measuring deformation of wind tunnel models. Model deformation systems use targets mounted or painted on the surface of the model to identify, known positions, and photogrammetric methods are used to calculate 3-D positions of the targets on the model from digital 2-D images. Under ideal conditions, the reflective targets are placed against a dark background and provide high-contrast images, aiding in target recognition. However, glints of light reflecting from the model surface, or reduced contrast caused by light source or model smoothness constraints, can compromise accurate target determination using current algorithmic methods. The paper describes a technique using a neural network and image processing technologies which increases the reliability of target recognition systems. Unlike algorithmic methods, the neural network can be trained to identify the characteristic patterns that distinguish targets from other objects of similar size and appearance and can adapt to changes in lighting and environmental conditions.
Target recognition using neural networks for model deformation measurements
IJCNN, International Joint Conference on Neural Networks, 1999 ; 5 ; 3152-3155
1999
4 Seiten, 3 Quellen
Conference paper
English
Target recognition and classification using neural networks
Tema Archive | 2002
|Neural networks for target recognition
Tema Archive | 1991
|Neural networks for automatic target recognition
Tema Archive | 1990
|Automatic target recognition using neural networks (invited Paper) [3466-37]
British Library Conference Proceedings | 1998
|