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Proceedings Paper

Pattern Recognition Using Shift Invariant Fourier-Mellin Descriptors And A Back-Propagation Net
Author(s): Claude Lejeune; Yunlong Sheng; Henri H. Arsenault
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Paper Abstract

Fourier-Mellin filters are used to generate invariant feature descriptors. The position of the maximum correlation output for each filter is used to create a distance vector. This vector is invariant under translation, rotation, change of scale and intensity of the input object. This method is applied to seven objects and the resulting, vectors are fed to a neural network for recognition. Of the seven objects, five are used to train the network. A three-layer feed-forward network, trained with the back-propagation algorithm is employed. Results show that distance vectors are suitable inputs for recognition by a neural network. The network learns the associations and recognizes the objects.

Paper Details

Date Published: 5 February 1990
PDF: 10 pages
Proc. SPIE 1151, Optical Information Processing Systems and Architectures, (5 February 1990); doi: 10.1117/12.962226
Show Author Affiliations
Claude Lejeune, Universite Laval (Canada)
Yunlong Sheng, Universite Laval (Canada)
Henri H. Arsenault, Universite Laval (Canada)


Published in SPIE Proceedings Vol. 1151:
Optical Information Processing Systems and Architectures
Bahram Javidi, Editor(s)

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