2-D object recognition using Fourier Mellin transform and a MLP network

Pattern recognition involves the correct recognition of an object irrespective of rotation, scale and translation. In this paper the authors have come up with a recognition scheme, that has shown 100% recognition rate for all rotation, translation and tolerates a scale factor from 1/2 to 2. The use of the Fourier Mellin transform to get features invariant to rotation, scale and translation has been attempted previously. The contribution of this paper is in the use of neural networks to classify the invariant patterns obtained by the use of FMT, thereby providing robustness to the whole scheme. The efficiency of such a scheme can be judged by the high recognition rate obtained even for partially occluded images.