Pattern recognition digital systems using the Z-Fisher transform
Carolina Barajas-Garc, Josué Álvarez-Borrego · 2015
In this work is presented a pattern recognition image descriptor invariant to rotation, scale and translation (RST), which classify images using the Z-Fisher transform. A binary rings mask is generated using the Fourier transform. The normalized analytic Fourier-Mellin amplitude spectrum is filtered with that mask to build 1D signature. The signatures comparison of the problem image and the target are done by the Pearson correlation coecient (PCC). In general, those PCC values do not satisfy a normal distribution, hence the Fisher’s Z distribution is employed to determine the confidence level of the RST invariant descriptor. The descriptor presents a confidence level of 95%.