Invariant object recognition using radon and fourier transforms

Authors:
Guangyi Chen;Tien Dai Bui;Adam Krzyzak;Yongjia Zhao
Affiliations:
Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;State Key Lab. of Virtual Reality Technology and Systems, Beihang University, Beijing, P.R. China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

In this paper, an invariant algorithm for object recognition is proposed by using the Radon and Fourier transforms. It has been shown that this algorithm is invariant to the translation and rotation of pattern images. The scaling invariance can be achieved by the standard normalization techniques. Our algorithm works even when the center of the pattern object is not aligned well. This advantage is because the Fourier spectra are invariant to spatial shift in the radial direction whereas existing methods assume the centroids are aligned exactly. Experimental results show that the proposed method is better than the Zernike's moments, the dual-tree complex wavelet (DTCWT) moments, and the auto-correlation wavelet moments for one aircraft database and one shape database.