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In this paper we propose a supervised object recognition method using newglobal features and inspired by the model of the human primary visual cortex V1as the semidiscrete roto-translation group $SE(2,N) = \mathbb Z_N\rtimes\mathbb R^2$. The proposed technique is based on generalized Fourierdescriptors on the latter group, which are invariant to natural geometrictransformations (rotations, translations). These descriptors are then used tofeed an SVM classifier. We have tested our method against the COIL-100 imagedatabase and the ORL face database, and compared it with other techniques basedon traditional descriptors, global and local. The obtained results have shownthat our approach looks extremely efficient and stable to noise, in presence ofwhich it outperforms the other techniques analyzed in the paper.

Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition