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RAMANUJAN SUMS FOR IMAGE PATTERN ANALYSIS

Department of Computer Science and Software Engineering, Concordia University, 1455 de Maisonneuve West, Montreal, Quebec, Canada H3G 1M8, Canada

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SRIDHAR KRISHNAN

Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada M5B 2K3, Canada

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, and

TIEN D. BUI

Department of Computer Science and Software Engineering, Concordia University, 1455 de Maisonneuve West, Montreal, Quebec, Canada H3G 1M8, Canada

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https://doi.org/10.1142/S0219691314500039Cited by:3 (Source: Crossref)

Abstract

Ramanujan Sums (RS) have been found to be very successful in signal processing recently. However, as far as we know, the RS have not been applied to image analysis. In this paper, we propose two novel algorithms for image analysis, including moment invariants and pattern recognition. Our algorithms are invariant to the translation, rotation and scaling of the 2D shapes. The RS are robust to Gaussian white noise and occlusion as well. Our algorithms compare favourably to the dual-tree complex wavelet (DTCWT) moments and the Zernike's moments in terms of correct classification rates for three well-known shape datasets.

Keywords:

AMSC: 68T10, 91C20, 62H35

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