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5 February 1990 Pattern Recognition Using Shift Invariant Fourier-Mellin Descriptors And A Back-Propagation Net
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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.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Claude Lejeune, Yunlong Sheng, and Henri H. Arsenault "Pattern Recognition Using Shift Invariant Fourier-Mellin Descriptors And A Back-Propagation Net", Proc. SPIE 1151, Optical Information Processing Systems and Architectures, (5 February 1990); https://doi.org/10.1117/12.962226
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