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Proceedings Paper

Shape discrimination using invariant Fourier representation and a neural network classifier
Author(s): Hsien-Huang Peter Wu; Robert A. Schowengerdt
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Paper Abstract

A neural network approach for classification of images represented by translation, scale, and rotation invariant features is presented. The invariant features are the Fourier descriptors (FDs) derived from the boundary (shape) of the object. The network is a multilayer perceptron (MLP) classifier with one hidden layer and back propagation training (MLP-BP). Performance of the MLP algorithm is compared to optimal curve matching (OCM) for the recognition of mechanical tools. The test data were 14 objects with eight images per object, each image having significant differences in scaling, translation, and rotation. Only 10 harmonics of the 1024 FD coefficients were used as the input vector. The neural network approach proved to be more stable and faster than the optimal curve matching algorithm in classifying the objects after the training phase. The simple calculations needed for the Fourier descriptors and the small number of coefficients needed to represent the boundary result in an efficient system, excluding training, which can be done off-line. Results are shown comparing the classification accuracy of the OCM method with the MLP-BP algorithm using different size training sets. The method can be extended to any patterns that can be discriminated by shape information.

Paper Details

Date Published: 1 October 1991
PDF: 8 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48374
Show Author Affiliations
Hsien-Huang Peter Wu, Univ. of Arizona (United States)
Robert A. Schowengerdt, Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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