Title :
Shape matching of 3D contours using normalized Fourier descriptors
Author :
Zhang, Hao ; Fiume, Eugene
Author_Institution :
Dept. of Comput. Sci., Univ. of Toronto, Ont., Canada
fDate :
6/24/1905 12:00:00 AM
Abstract :
In this paper, we develop a simple, eigenspace matching algorithm for closed 3D contours. Our algorithm relies on a novel method which normalizes the Fourier descriptors (FDs) of a 3D contour with respect to two of its FD coefficients corresponding to the lowest non-zero frequencies. The remaining matching task only involves vertex shift and rotation about the z-axis. Our approach is inspired by the observation that the traditional Fourier transform of a 1D signal is equivalent to the decomposition of the signal into a linear combination of the eigenvectors of a smoothing operator. It turns out that our FD normalization is equivalent to aligning the limit plane approached by the sequence of progressively smoothed 3D contours with the xy-plane
Keywords :
Fourier transforms; computational geometry; computer vision; edge detection; eigenvalues and eigenfunctions; image matching; stereo image processing; closed 3D contours; eigenspace matching algorithm; eigenvectors; lowest nonzero frequencies; normalization; normalized Fourier descriptors; progressively smoothed 3D contour; shape matching; smoothing operator; vertex rotation; vertex shift; z-axis; Computer science; Computer vision; Educational institutions; Electronic mail; Fourier transforms; Frequency; Image edge detection; Object recognition; Shape measurement; Smoothing methods;
Conference_Titel :
Shape Modeling International, 2002. Proceedings
Conference_Location :
Banff, Alta.
Print_ISBN :
0-7695-1546-0
DOI :
10.1109/SMI.2002.1003554