Rotation invariant object classification using fast Fourier transform features
Mehmet Çelenk, Srinivasa R. Datari · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
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.