Invariant pattern recognition using the RFM descriptor

A pattern descriptor invariant to rotation, scaling, translation (RST), and robust to additive noise is proposed by using the Radon, Fourier, and Mellin transforms. The Radon transform converts the RST transformations applied on a pattern image into transformations in the radial and angular coordinates of the pattern's Radon image. These beneficial properties of the Radon transform make it an useful intermediate representation for the extraction of invariant features from pattern images for the purpose of indexing/matching. In this paper, invariance to RST is obtained by applying the 1D Fourier–Mellin and discrete Fourier transforms on the radial and angular coordinates of the pattern's Radon image respectively. The implementation of the proposed descriptor is reasonably fast and correct, based mainly on the fusion of the Radon and Fourier transforms and on a modification of the Mellin transform. Theoretical arguments validate the robustness of the proposed descriptor to additive noise and empirical evidence on both occlusion/deformation and noisy datasets shows its effectiveness.

► We use Radon, Fourier, and Mellin transforms to represent images for recognition.
► The representation is invariant to rotation, scaling, and translation.
► It is fast in implementation and robust to white and salt-pepper noise.
► Its superiority over other methods is demonstrated on noisy/deformation datasets.