Proceedings Paper
Feature extraction from photographic images using a hybrid neural network| Format | Member Price | Non-Member Price |
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Paper Abstract
A new method for extracting features from photographic images has been developed. The input image is through a pulse coupled neural network transformed to a set of signatures, well suited for classification by unsupervised neural networks. A strategy using multiple self-organizing feature maps in a hierarchical manner is developed. With this approach, using a certain degree of supervision, an acceptable classification is obtained when applied to test images. The method is applied to license plate recognition.
Paper Details
Date Published: 22 March 1999
PDF: 11 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343053
Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)
PDF: 11 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343053
Show Author Affiliations
Vlatko Becanovic, Royal Institute of Technology (Sweden)
Martin Kermit, Ostfold College (Norway)
Martin Kermit, Ostfold College (Norway)
Age J. Eide, Ostfold College (Norway)
Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)
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