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Noise analysis of two pattern recognition methodologies using binary masks based on the fractional Fourier transform

Noise often corrupts images; therefore, it is essential to know the performance capability of a pattern recognition algorithm for images affected by it. In this work, a complete analysis of two methodologies is performed when images are affected by Gaussian and salt and pepper noise. The two methods use the nonlinear correlation of signatures. A signature is a onedimensional vector that represents each image, and it is obtained using a binary mask created based on the fractional Fourier transform (FRFT). In the first methodology, a spectral image it is used as the input to the system. The spectral image is the modulus of the Fourier transform (FT) of the image processed. The binary mask is generated from the real part of the FRFT of the spectral image. The signature is constructed by sampling the modulus of the FRFT of the spectral image with the mask. In the second methodology, the image is the input to the system, and the binary mask is obtained from the real part of the FRFT of the image. The signature, in this case, is obtained by sampling the modulus of the FT of the image with the binary mask. Each method was tested using the discrimination coefficient metric.