Orientation detection: Comparison of moments with back propagation

The authors describe two different approaches to object orientation detection: one method uses first- and second-order moments, while the other uses a multilayer perceptron network trained by back error propagation. A comparison between these methods shows that the neural network is able to generalize the trained orientations for different classes of objects and affords better control in determining the orientation at the pick-up point. Maximum orientation resolution is achieved economically by using a form of coarse coding, in which the output excitations for all orientations are spread out among neighboring output nodes