This paper reports on an experimental comparison of two visual object recognition methods: a radial basis function network (RBFN) which is an artificial neural network, and a synthetic discriminant function network (SDFN) which classifies objects statistically via analysis with optimal spatial filters. Both methods require training with a set of images representative of the objects to be recognized. A comparative performance analysis was performed after training both networks with the same image sets. The algorithms were implemented on a Pentium-class PC under MS Windows NT 4.0. Training images were captured from a color CCD cameras with standard NTSC resolution. Experiments were performed on both methods to determine the number of images per object necessary to train the networks, to estimate the two networks' accuracy of recognition, and to characterize their tolerance to image noise. It was found that when presented with a new image of one of the objects, RBFNs are more accurate at recognition than SDFNs. However, SDFNs are slightly more accurate in the presence of additive noise. Under the conditions of the experiments, RBFNs were found to provide an overall minimum classification accuracy of close to ninety percent
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