A COMPARISON BETWEEN FOURIER-MELLIN DESCRIPTORS AND MOMENT BASED FEATURES FOR INVARIANT OBJECT RECOGNITION USING NEURAL NETWORKS

Two algorithms for invariant object recognition are compared using a neural network and two statistical classifiers as classifiers for features taken from noiseless and noisy images. It was found that the Fourier-Mellin descriptors perform as well as moment based features for noiseless images but perform significantly better when noise is added. Neural networks perform better than statistical classifiers when noise is added. It was also found that Fourier-Mellin descriptors are very sensitive to uncertainty in the position of the object centroid whereas the moment based features provide good performance when the object is not presented at the centroid.