Rotation invariant object classification using fast Fourier transform features

Mehmet Celenk; Srinivasa Rao Datari

doi:10.1117/12.45514

1 March 1991 Rotation invariant object classification using fast Fourier transform features

Mehmet Celenk, Srinivasa Rao Datari

Proceedings Volume 1468, Applications of Artificial Intelligence IX; (1991) https://doi.org/10.1117/12.45514
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

This paper describes a position and rotation invariant fast object classification scheme. A parallel region growing technique is used to detect objects in binary images. 2D fast Fourier transform (FFT) is applied to each object region after translating the origin of the image coordinate system to the object center and aligning the image coordinate axes with the object's principal axes. The first five components from the principal lobe of the Fourier spectrum of each object are selected as characteristic features for minimum-distance classification. For time efficiency, region growing and 2D FFT computations were performed on a 16-node hypercube processor.

Citation Download Citation

Mehmet Celenk and Srinivasa Rao Datari "Rotation invariant object classification using fast Fourier transform features", Proc. SPIE 1468, Applications of Artificial Intelligence IX, (1 March 1991); https://doi.org/10.1117/12.45514

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