27 March 1997 Rotation, scale, and translation invariant pattern recognition using feature extraction
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Abstract
A rotation, scale and translation invariant pattern recognition technique is proposed.It is based on Fourier- Mellin Descriptors (FMD). Each FMD is taken as an independent feature of the object, and a set of those features forms a signature. FMDs are naturally rotation invariant. Translation invariance is achieved through pre- processing. A proper normalization of the FMDs gives the scale invariance property. This approach offers the double advantage of providing invariant signatures of the objects, and a dramatic reduction of the amount of data to process. The compressed invariant feature signature is next presented to a multi-layered perceptron neural network. This final step provides some robustness to the classification of the signatures, enabling good recognition behavior under anamorphically scaled distortion. We also present an original feature extraction technique, adapted to optical calculation of the FMDs. A prototype optical set-up was built, and experimental results are presented.
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Donald Prevost, Donald Prevost, Michel Doucet, Michel Doucet, Alain Bergeron, Alain Bergeron, Luc Veilleux, Luc Veilleux, Paul C. Chevrette, Paul C. Chevrette, Denis J. Gingras, Denis J. Gingras, } "Rotation, scale, and translation invariant pattern recognition using feature extraction", Proc. SPIE 3073, Optical Pattern Recognition VIII, (27 March 1997); doi: 10.1117/12.270371; https://doi.org/10.1117/12.270371
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