Shift and scale‐invariant correlation for pattern recognition based on the bidimensional empirical mode decomposition with noise robustness

Abstract

A novel filter is proposed to improve the noise robustness and discrimination capability for shift and scale invariant pattern recognition. This filter is a combination of mellin radial harmonic filter (RHF) and the bidimensional empirical mode decomposition. The basic principle of this method is to make use of partial reconstructions of the image by the relevant intrinsic mode functions corresponding to the most important structures of the image. A criterion is proposed to determine the proper number of intrinsic mode functions to be discarded for denoising by discussing the characteristic of the noise. The proposed filter provides a wider allowable scale change of the object. Within this range, the correlation peak intensity is relatively uniform even in the case of noise. This proposed filter has been tested experimentally to confirm the result from numerical simulations for cases with and without additive white Gaussian noise.