Shape classification using multiscale Fourier-based description in 2-D space

In shape recognition, the boundary and exterior parts are amongst the most discriminative features. In this paper, we propose new multiscale Fourier-based object descriptors in 2-D space, which represents the boundary and exterior parts of an object more than the central part. This representation is based on using a high-pass Gaussian filter at different scales. The proposed algorithm makes descriptors size, translation and rotation invariant as well as increasing discriminative power and immunity to noise. In comparison, the new algorithm performs better than elliptic Fourier descriptors and Zernike moments with respect to increasing noise.